Mock feed data - read in occupany modeling results and filter by Bray-Curtis
The data decontamination includes two steps: 1. species occupancy detection modeling 2. Bray-Curtis dissimilarity analysis
Here, I’ll read in the data from the SODM analysis, assess which ASVs will be removed based on that, and then move onto calculating the Bray-Curtis dissimilarities across sample replicates. Finally, the repicates that are more dissimilar (> 0.49) than similar to one another will be removed.
Load data and libraries
library(tidyverse)
library(stringi)
library(vegan)
library(reshape2)
library(textshape)
library(rlist)
As I did with the reference mock communities, the way I’m thinking about this is to create a .txt file with a list of the file names and loci and to read that in, but ideally, I would also have a column for the sample name so that I could actually properly filter for each locus-sample when anti-joining with the dataframe.
Before getting into all of that regex stuff, probably best to take a quick look at whether there are many ASVs that get removed for the data from F1 or the mock feed pools.
# Full dataframe
features_w_taxonomy <- readRDS("../data/feature_df_no_eDNA_metazoa_only.rds")
# and newly updated, the same dataframes, but using a 98% identity threshold for any species-level assignments
tax_df_spp_98 <- readRDS("../data/uncollapsed_taxonomy_spp_98.rds")
unique_tax_spp_98 <- readRDS("../data/unique_taxonomy_spp_98.rds")
# meta data for group and type assignments
meta <- read_csv("../data/jan_sample_list.csv")Parsed with column specification:
cols(
sample = [31mcol_character()[39m,
new_name = [31mcol_character()[39m,
type = [31mcol_character()[39m,
group = [31mcol_character()[39m,
filler = [31mcol_character()[39m,
perc_fishmeal = [32mcol_double()[39m
)# use the new name column to bind to the input file
feature_tax_name_revised <- features_w_taxonomy %>%
left_join(., meta, by = "sample") %>%
select(locus, seq, new_name, count) %>%
rename(sample = new_name)
Occupancy modeling data
Based on the SODM filtering, now I need to read those data back in for the mock feed data/mixtures and pools.
The files are located in this directory: /Users/dianabaetscher/Documents/git-repos/metabarcoding-methods/Rmd_fixed_db/csv_outputs/SODM
# get the names of the files
fdf <- read.table("~/Documents/git-repos/metabarcoding-methods/data/sodm_samples_loc.txt", stringsAsFactors = FALSE, header = TRUE) %>%
tbl_df()
dir <- "~/Documents/git-repos/metabarcoding-methods/Rmd_fixed_db/csv_outputs/SODM"
# cycle over them, read them and add the locus column on each.
# at the end, bind them together.
mock_feed_sodm_df <- lapply(1:nrow(fdf), function(i) {
read_csv(paste(dir, fdf$file[i], sep = "/"), col_names = TRUE) %>%
mutate(locus = fdf$locus[i]) %>%
mutate(sample = fdf$sample[i]) %>%
select(locus, sample, everything())
}) %>%
bind_rows() %>%
group_by(locus, sample, seq) %>%
mutate(max_estimate = sum(estimate,std.error)) %>%
mutate(max_estimate = ifelse(max_estimate > 1, 1, max_estimate)) %>% # make 1 the maximum probability.
select(locus, seq, estimate, std.error, max_estimate)Parsed with column specification:
cols(
term = [31mcol_character()[39m,
estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
)
Parsed with column specification:
cols(
term = [31mcol_character()[39m,
estimate = [32mcol_double()[39m,
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Parsed with column specification:
cols(
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Parsed with column specification:
cols(
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Parsed with column specification:
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Parsed with column specification:
cols(
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Parsed with column specification:
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Parsed with column specification:
cols(
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seq = [31mcol_character()[39m
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Parsed with column specification:
cols(
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estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
)
Parsed with column specification:
cols(
term = [31mcol_character()[39m,
estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
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Parsed with column specification:
cols(
term = [31mcol_character()[39m,
estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
)
Parsed with column specification:
cols(
term = [31mcol_character()[39m,
estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
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Parsed with column specification:
cols(
term = [31mcol_character()[39m,
estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
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Parsed with column specification:
cols(
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estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
)
Parsed with column specification:
cols(
term = [31mcol_character()[39m,
estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
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Parsed with column specification:
cols(
term = [31mcol_character()[39m,
estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
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Parsed with column specification:
cols(
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estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
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Parsed with column specification:
cols(
term = [31mcol_character()[39m,
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std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
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Parsed with column specification:
cols(
term = [31mcol_character()[39m,
estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
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Parsed with column specification:
cols(
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estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
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Parsed with column specification:
cols(
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Parsed with column specification:
cols(
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Parsed with column specification:
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Parsed with column specification:
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Parsed with column specification:
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Parsed with column specification:
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Parsed with column specification:
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Parsed with column specification:
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Parsed with column specification:
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Parsed with column specification:
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Parsed with column specification:
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Parsed with column specification:
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estimate = [32mcol_double()[39m,
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Parsed with column specification:
cols(
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Parsed with column specification:
cols(
term = [31mcol_character()[39m,
estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
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Parsed with column specification:
cols(
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estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
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Parsed with column specification:
cols(
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estimate = [32mcol_double()[39m,
std.error = [32mcol_double()[39m,
seq = [31mcol_character()[39m
)
Adding missing grouping variables: `sample`So these are the ASVs that I would be filtering
mock_feed_sodm_asvs_removed <- mock_feed_sodm_df %>%
filter(max_estimate < 0.8)
mock_feed_sodm_asvs_removedThere aren’t very many of these, but some…
so for consistency sake, I suppose better to remove them.
Anti-join the ASVs to remove with the full dataframe
mock_feed_feature_df <- feature_tax_name_revised %>%
filter(!str_detect(sample, "VRP") & !str_detect(sample, "FRP") & !str_detect(sample, "extr_blank")) %>%
separate(sample, into = c("filler", "perc_fishmeal", "extract", "pcr_rep"), sep = "_", remove = FALSE) %>%
unite(feed, filler, perc_fishmeal, sep = "_", remove = TRUE) %>%
select(-extract, -pcr_rep) %>%
mutate(feed = ifelse(str_detect(feed, "MFP"), "MFP", feed)) %>%
mutate(feed = ifelse(str_detect(feed, "MEP"), "MEP", feed))Expected 4 pieces. Missing pieces filled with `NA` in 15887 rows [29386, 29387, 29388, 29389, 29390, 29391, 29392, 29393, 29394, 29395, 29396, 29397, 29398, 29399, 29400, 29401, 29402, 29403, 29404, 29405, ...].# check that all of those modifications took effect - basically to make a column that I can use for an anti-join with the SODM filtered data
mock_feed_feature_df %>%
filter(str_detect(sample, "MFP"))Use an anti-join to remove the 27 ASVs from the full feature table.
mock_feed_sodm_asvs_removed_dataframe <- mock_feed_feature_df %>%
anti_join(., mock_feed_sodm_asvs_removed, by = c("locus", "seq", "feed" = "sample")) %>%
select(-feed) # remove the column that I used for the join since I don't need it moving forward
Save the output
saveRDS(mock_feed_sodm_asvs_removed_dataframe, "../data/mockfeed_sodm_asvs_removed_feature_df.rds", compress = "xz")
Dissimilarity analyses
Using the dataframe generated above…
Dissimilarity analyses include a PERMANOVA on Bray-Curtis Jaccard similarities (minimizing the effect of PCR amplification bias)
The process for looking at the dissimilarity among replicates is to: 1. read in data that has been cleaned up using the occupancy modeling 2. create a community matrix (per locus) 3. standardize data across replicates (fct decostand) 4. generate Bray-Curtis distances (fct vegdist) 4a. Are any replicates more dissimilar than similar? 5. generate NMDS plots from distance matrix (fct metaMDS)
Outcomes: Based on the NMDS plots and Bray-Curtis dissimilarity index, I will generate a list of samples to remove.
source("../R/metabarcoding-funcs.R")Cycle over a list of the loci
# grab the names of the loci from the full dataframe
locs <- c("cep", "mifish", "nsCOIFo", "fishminiA")
# how many feed samples?
mock_feed_sodm_asvs_removed_dataframe %>%
select(sample) %>%
unique()NANOTE: MEP and MFP have a diff number of parts than the mock feeds… 9 replicates vs. 6 replicates, so splitting the sample into reference, pool, and pcr replicate will look different.
Bray-Curtis function
Output from the function should go into a directory called csv_outputs/bray_dissimilar/mockfeeds/
If this directory doesn’t exist, the function will create it.
bray_nmds_mock_feed(mock_feed_sodm_asvs_removed_dataframe, loc = "cep", site = "F1_0")'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0
Run 1 stress 0
... Procrustes: rmse 0.08306481 max resid 0.1500837
Run 2 stress 8.393474e-05
... Procrustes: rmse 0.0650365 max resid 0.1016619
Run 3 stress 9.601771e-05
... Procrustes: rmse 0.1508988 max resid 0.2618136
Run 4 stress 0
... Procrustes: rmse 0.09565963 max resid 0.1719531
Run 5 stress 9.762288e-05
... Procrustes: rmse 0.1508948 max resid 0.2618009
Run 6 stress 0
... Procrustes: rmse 0.05516981 max resid 0.08298733
Run 7 stress 0.005052253
Run 8 stress 9.545577e-05
... Procrustes: rmse 0.1508969 max resid 0.2618038
Run 9 stress 6.304464e-05
... Procrustes: rmse 0.06759018 max resid 0.1260851
Run 10 stress 0
... Procrustes: rmse 0.06590909 max resid 0.1190405
Run 11 stress 3.912468e-05
... Procrustes: rmse 0.07810179 max resid 0.1189405
Run 12 stress 0.1964001
Run 13 stress 0
... Procrustes: rmse 0.05915666 max resid 0.1147621
Run 14 stress 1.210162e-05
... Procrustes: rmse 0.1275159 max resid 0.2214862
Run 15 stress 0.1964001
Run 16 stress 7.15227e-05
... Procrustes: rmse 0.06823941 max resid 0.09896617
Run 17 stress 7.930743e-05
... Procrustes: rmse 0.1418135 max resid 0.2454811
Run 18 stress 0.2181459
Run 19 stress 0.004773638
Run 20 stress 0
... Procrustes: rmse 0.05536676 max resid 0.1114028
Run 21 stress 0
... Procrustes: rmse 0.05288896 max resid 0.07930563
Run 22 stress 0
... Procrustes: rmse 0.07216272 max resid 0.1259475
Run 23 stress 0
... Procrustes: rmse 0.09203779 max resid 0.1867488
Run 24 stress 9.716907e-05
... Procrustes: rmse 0.1509008 max resid 0.2618182
Run 25 stress 0
... Procrustes: rmse 0.05973701 max resid 0.09446323
Run 26 stress 0
... Procrustes: rmse 0.08943409 max resid 0.1756147
Run 27 stress 0
... Procrustes: rmse 0.0844327 max resid 0.14415
Run 28 stress 0
... Procrustes: rmse 0.1119332 max resid 0.2013292
Run 29 stress 1.980986e-05
... Procrustes: rmse 0.06570038 max resid 0.1169952
Run 30 stress 0.2761354
Run 31 stress 0
... Procrustes: rmse 0.07770718 max resid 0.1074483
Run 32 stress 0
... Procrustes: rmse 0.06254715 max resid 0.09887051
Run 33 stress 0
... Procrustes: rmse 0.04758941 max resid 0.06036237
Run 34 stress 0.1964001
Run 35 stress 0.1964001
Run 36 stress 0.1964001
Run 37 stress 0.2761343
Run 38 stress 0
... Procrustes: rmse 0.05251142 max resid 0.0876186
Run 39 stress 0.004699463
Run 40 stress 0.1964001
Run 41 stress 0
... Procrustes: rmse 0.1306747 max resid 0.2260125
Run 42 stress 0
... Procrustes: rmse 0.09506064 max resid 0.1884457
Run 43 stress 0
... Procrustes: rmse 0.07195941 max resid 0.1353737
Run 44 stress 9.83279e-05
... Procrustes: rmse 0.1509008 max resid 0.261823
Run 45 stress 0
... Procrustes: rmse 0.06384242 max resid 0.1159743
Run 46 stress 3.978645e-05
... Procrustes: rmse 0.06397951 max resid 0.08485267
Run 47 stress 9.82312e-05
... Procrustes: rmse 0.150878 max resid 0.2617063
Run 48 stress 8.665296e-05
... Procrustes: rmse 0.08506109 max resid 0.1365683
Run 49 stress 9.70663e-05
... Procrustes: rmse 0.1508964 max resid 0.2618059
Run 50 stress 9.490215e-05
... Procrustes: rmse 0.1179302 max resid 0.2025423
*** No convergence -- monoMDS stopping criteria:
3: no. of iterations >= maxit
38: stress < smin
8: stress ratio > sratmax
1: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data[[1]]
[[1]]$x
[1] 0.08235372 0.16922326 0.13399688 0.18851684 0.11184361 0.16554871 0.11063268 0.18048591
[9] 0.10709883 0.16577753 0.16120027 0.14635979 0.20065758 0.14547214 0.16433578
[[1]]$y
[1] 0.03627004 0.20772785 0.08460255 0.22588311 0.06460388 0.17297458 0.06400572 0.20985487
[9] 0.04354659 0.18040217 0.16482254 0.15153341 0.25976659 0.10136720 0.16645123
[[1]]$yf
[1] 0.03627004 0.20772785 0.08460255 0.22588311 0.06460388 0.17297458 0.06400572 0.20985487
[9] 0.04354659 0.18040217 0.16482254 0.15153341 0.25976659 0.10136720 0.16645123
[[2]]
# analyze the four loci for mock feed sample F0_100
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F0_100")'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 6.797137e-05
Run 1 stress 0.1557467
Run 2 stress 0.1557467
Run 3 stress 7.562435e-05
... Procrustes: rmse 0.08030631 max resid 0.1188797
Run 4 stress 8.966116e-05
... Procrustes: rmse 0.1487333 max resid 0.212373
Run 5 stress 0.1557467
Run 6 stress 0
... New best solution
... Procrustes: rmse 0.1918413 max resid 0.2486181
Run 7 stress 0.2761342
Run 8 stress 0
... Procrustes: rmse 0.1630701 max resid 0.2724451
Run 9 stress 7.614869e-05
... Procrustes: rmse 0.1670793 max resid 0.2436565
Run 10 stress 0
... Procrustes: rmse 0.09712496 max resid 0.1316051
Run 11 stress 0
... Procrustes: rmse 0.0999803 max resid 0.1220419
Run 12 stress 0.1967694
Run 13 stress 0
... Procrustes: rmse 0.1549404 max resid 0.2672777
Run 14 stress 2.45985e-05
... Procrustes: rmse 0.2330788 max resid 0.3910971
Run 15 stress 0
... Procrustes: rmse 0.1467988 max resid 0.2363045
Run 16 stress 0
... Procrustes: rmse 0.1776624 max resid 0.285541
Run 17 stress 0
... Procrustes: rmse 0.1316287 max resid 0.2212684
Run 18 stress 0.2269841
Run 19 stress 0
... Procrustes: rmse 0.08066602 max resid 0.122513
Run 20 stress 0
... Procrustes: rmse 0.04044139 max resid 0.055289
Run 21 stress 0.1557467
Run 22 stress 0
... Procrustes: rmse 0.1626213 max resid 0.261945
Run 23 stress 0
... Procrustes: rmse 0.08670665 max resid 0.1182422
Run 24 stress 0
... Procrustes: rmse 0.1210269 max resid 0.213731
Run 25 stress 0
... Procrustes: rmse 0.1632461 max resid 0.272867
Run 26 stress 5.778743e-05
... Procrustes: rmse 0.219172 max resid 0.3830126
Run 27 stress 0
... Procrustes: rmse 0.2324768 max resid 0.3781966
Run 28 stress 0
... Procrustes: rmse 0.1949482 max resid 0.2730188
Run 29 stress 0
... Procrustes: rmse 0.1068732 max resid 0.166953
Run 30 stress 0
... Procrustes: rmse 0.2466887 max resid 0.3894845
Run 31 stress 4.054386e-05
... Procrustes: rmse 0.1660896 max resid 0.258395
Run 32 stress 8.558735e-05
... Procrustes: rmse 0.2336236 max resid 0.4060155
Run 33 stress 0
... Procrustes: rmse 0.1177261 max resid 0.181473
Run 34 stress 0
... Procrustes: rmse 0.1320465 max resid 0.1964872
Run 35 stress 0
... Procrustes: rmse 0.1238575 max resid 0.1643499
Run 36 stress 0.1967694
Run 37 stress 6.850994e-05
... Procrustes: rmse 0.2105404 max resid 0.3336422
Run 38 stress 0
... Procrustes: rmse 0.1778579 max resid 0.2988372
Run 39 stress 0
... Procrustes: rmse 0.2457966 max resid 0.381166
Run 40 stress 0
... Procrustes: rmse 0.1979047 max resid 0.3160818
Run 41 stress 3.263647e-05
... Procrustes: rmse 0.08710919 max resid 0.128847
Run 42 stress 0
... Procrustes: rmse 0.157559 max resid 0.2497317
Run 43 stress 0
... Procrustes: rmse 0.145525 max resid 0.247641
Run 44 stress 0
... Procrustes: rmse 0.1654702 max resid 0.2621403
Run 45 stress 1.314595e-05
... Procrustes: rmse 0.08978755 max resid 0.1289944
Run 46 stress 0.2761355
Run 47 stress 0
... Procrustes: rmse 0.1952367 max resid 0.2994856
Run 48 stress 0
... Procrustes: rmse 0.0486865 max resid 0.06676525
Run 49 stress 0
... Procrustes: rmse 0.1063841 max resid 0.196372
Run 50 stress 7.812381e-05
... Procrustes: rmse 0.1775323 max resid 0.2944729
*** No convergence -- monoMDS stopping criteria:
41: stress < smin
6: stress ratio > sratmax
3: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient dataRun 0 stress 0
Run 1 stress 0.1557467
Run 2 stress 0
... Procrustes: rmse 0.216707 max resid 0.3284185
Run 3 stress 0
... Procrustes: rmse 0.1239389 max resid 0.2093844
Run 4 stress 8.438805e-05
... Procrustes: rmse 0.2226681 max resid 0.3391351
Run 5 stress 0.2130989
Run 6 stress 7.976189e-05
... Procrustes: rmse 0.2228066 max resid 0.3525509
Run 7 stress 0
... Procrustes: rmse 0.2430633 max resid 0.3746751
Run 8 stress 0.2269842
Run 9 stress 0
... Procrustes: rmse 0.1875387 max resid 0.2526191
Run 10 stress 0
... Procrustes: rmse 0.1827241 max resid 0.2771838
Run 11 stress 2.442165e-05
... Procrustes: rmse 0.2293933 max resid 0.3454122
Run 12 stress 6.515037e-05
... Procrustes: rmse 0.2440448 max resid 0.3444438
Run 13 stress 0
... Procrustes: rmse 0.1821596 max resid 0.2501774
Run 14 stress 0
... Procrustes: rmse 0.2159835 max resid 0.2765069
Run 15 stress 0.2761338
Run 16 stress 0
... Procrustes: rmse 0.1840612 max resid 0.2801757
Run 17 stress 0
... Procrustes: rmse 0.1889659 max resid 0.2538683
Run 18 stress 0
... Procrustes: rmse 0.196481 max resid 0.2786979
Run 19 stress 0
... Procrustes: rmse 0.2018784 max resid 0.2885867
Run 20 stress 0
... Procrustes: rmse 0.1808124 max resid 0.2383733
Run 21 stress 0.1557467
Run 22 stress 0.2269841
Run 23 stress 9.955098e-05
... Procrustes: rmse 0.1965028 max resid 0.2739226
Run 24 stress 0
... Procrustes: rmse 0.2067467 max resid 0.37563
Run 25 stress 0.2130994
Run 26 stress 0.1557467
Run 27 stress 0
... Procrustes: rmse 0.1873015 max resid 0.3136669
Run 28 stress 9.662342e-05
... Procrustes: rmse 0.1844764 max resid 0.2552976
Run 29 stress 1.520534e-05
... Procrustes: rmse 0.2022146 max resid 0.2753304
Run 30 stress 0.2181459
Run 31 stress 9.723643e-05
... Procrustes: rmse 0.1075217 max resid 0.1644708
Run 32 stress 0.1967694
Run 33 stress 0
... Procrustes: rmse 0.2105681 max resid 0.2807321
Run 34 stress 0
... Procrustes: rmse 0.2094619 max resid 0.3026186
Run 35 stress 8.59255e-05
... Procrustes: rmse 0.06414821 max resid 0.07604778
Run 36 stress 0.2181459
Run 37 stress 0
... Procrustes: rmse 0.2116545 max resid 0.311221
Run 38 stress 0.2672679
Run 39 stress 0
... Procrustes: rmse 0.1587251 max resid 0.2388922
Run 40 stress 0.0002749278
... Procrustes: rmse 0.1736291 max resid 0.2525672
Run 41 stress 0
... Procrustes: rmse 0.2172166 max resid 0.3481989
Run 42 stress 0
... Procrustes: rmse 0.2329948 max resid 0.3424033
Run 43 stress 9.368023e-05
... Procrustes: rmse 0.1995462 max resid 0.3626213
Run 44 stress 0
... Procrustes: rmse 0.1559475 max resid 0.216891
Run 45 stress 7.549164e-05
... Procrustes: rmse 0.2081239 max resid 0.3095146
Run 46 stress 0
... Procrustes: rmse 0.232797 max resid 0.3320413
Run 47 stress 0
... Procrustes: rmse 0.2050156 max resid 0.3668122
Run 48 stress 0
... Procrustes: rmse 0.2286724 max resid 0.3317656
Run 49 stress 7.935514e-05
... Procrustes: rmse 0.2375795 max resid 0.3395622
Run 50 stress 0.2269841
*** No convergence -- monoMDS stopping criteria:
1: no. of iterations >= maxit
36: stress < smin
7: stress ratio > sratmax
6: scale factor of the gradient < sfgrmin
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0
Run 1 stress 0
... Procrustes: rmse 0.200917 max resid 0.2729433
Run 2 stress 0
... Procrustes: rmse 0.1792084 max resid 0.2435658
Run 3 stress 0
... Procrustes: rmse 0.1042875 max resid 0.1523804
Run 4 stress 0
... Procrustes: rmse 0.1695369 max resid 0.227262
Run 5 stress 0
... Procrustes: rmse 0.2000718 max resid 0.3332387
Run 6 stress 0.1970932
Run 7 stress 1.975794e-05
... Procrustes: rmse 0.139057 max resid 0.1892978
Run 8 stress 8.485461e-05
... Procrustes: rmse 0.160896 max resid 0.2064387
Run 9 stress 0.1967694
Run 10 stress 0.1967694
Run 11 stress 6.842862e-05
... Procrustes: rmse 0.227878 max resid 0.4086503
Run 12 stress 0
... Procrustes: rmse 0.1548529 max resid 0.2325908
Run 13 stress 9.267035e-05
... Procrustes: rmse 0.1227584 max resid 0.1668256
Run 14 stress 0
... Procrustes: rmse 0.1676108 max resid 0.2708664
Run 15 stress 0.2761338
Run 16 stress 9.729758e-05
... Procrustes: rmse 0.1137458 max resid 0.1614339
Run 17 stress 9.830937e-05
... Procrustes: rmse 0.136207 max resid 0.1934379
Run 18 stress 0
... Procrustes: rmse 0.170082 max resid 0.2581504
Run 19 stress 0
... Procrustes: rmse 0.1395374 max resid 0.1921986
Run 20 stress 0
... Procrustes: rmse 0.1771517 max resid 0.281075
Run 21 stress 0.1967694
Run 22 stress 9.593632e-05
... Procrustes: rmse 0.1757822 max resid 0.254933
Run 23 stress 0
... Procrustes: rmse 0.1679382 max resid 0.2445066
Run 24 stress 0
... Procrustes: rmse 0.215328 max resid 0.3876556
Run 25 stress 0.2672679
Run 26 stress 6.493077e-05
... Procrustes: rmse 0.2121383 max resid 0.3650302
Run 27 stress 0
... Procrustes: rmse 0.1669448 max resid 0.2566071
Run 28 stress 0.2181459
Run 29 stress 0.2761345
Run 30 stress 0.2181459
Run 31 stress 0
... Procrustes: rmse 0.1527509 max resid 0.2041601
Run 32 stress 0.2181459
Run 33 stress 0.1967694
Run 34 stress 0
... Procrustes: rmse 0.1592293 max resid 0.2547942
Run 35 stress 0
... Procrustes: rmse 0.09241022 max resid 0.1138217
Run 36 stress 8.603144e-05
... Procrustes: rmse 0.2188329 max resid 0.2805109
Run 37 stress 9.855702e-05
... Procrustes: rmse 0.2394081 max resid 0.4308188
Run 38 stress 0.2672326
Run 39 stress 0
... Procrustes: rmse 0.1646802 max resid 0.2675111
Run 40 stress 4.756819e-05
... Procrustes: rmse 0.2499768 max resid 0.3354394
Run 41 stress 0
... Procrustes: rmse 0.1700967 max resid 0.3107606
Run 42 stress 0.1967694
Run 43 stress 0
... Procrustes: rmse 0.1994484 max resid 0.3325655
Run 44 stress 0
... Procrustes: rmse 0.1817738 max resid 0.2790491
Run 45 stress 8.214697e-05
... Procrustes: rmse 0.1802124 max resid 0.3089334
Run 46 stress 0.1967694
Run 47 stress 0
... Procrustes: rmse 0.2311707 max resid 0.4127761
Run 48 stress 0.2269841
Run 49 stress 0
... Procrustes: rmse 0.1642674 max resid 0.2280852
Run 50 stress 7.171528e-05
... Procrustes: rmse 0.1727568 max resid 0.260029
*** No convergence -- monoMDS stopping criteria:
35: stress < smin
9: stress ratio > sratmax
6: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 1.883529e-05
Run 1 stress 0
... New best solution
... Procrustes: rmse 0.1189936 max resid 0.182086
Run 2 stress 0
... Procrustes: rmse 0.1251948 max resid 0.2458473
Run 3 stress 0
... Procrustes: rmse 0.1382548 max resid 0.2108325
Run 4 stress 0
... Procrustes: rmse 0.0677733 max resid 0.09909404
Run 5 stress 0.1967694
Run 6 stress 0
... Procrustes: rmse 0.1268782 max resid 0.2126362
Run 7 stress 9.768749e-05
... Procrustes: rmse 0.1401893 max resid 0.253471
Run 8 stress 0
... Procrustes: rmse 0.151565 max resid 0.1965376
Run 9 stress 0
... Procrustes: rmse 0.1570525 max resid 0.2336614
Run 10 stress 0
... Procrustes: rmse 0.1597817 max resid 0.2649448
Run 11 stress 0.2181459
Run 12 stress 0
... Procrustes: rmse 0.169898 max resid 0.2155338
Run 13 stress 0
... Procrustes: rmse 0.1659109 max resid 0.3556193
Run 14 stress 0
... Procrustes: rmse 0.1969086 max resid 0.2867465
Run 15 stress 8.772143e-05
... Procrustes: rmse 0.09760409 max resid 0.1242714
Run 16 stress 2.358302e-05
... Procrustes: rmse 0.1829463 max resid 0.268296
Run 17 stress 9.512092e-05
... Procrustes: rmse 0.127823 max resid 0.1718707
Run 18 stress 0
... Procrustes: rmse 0.1792446 max resid 0.3719052
Run 19 stress 0
... Procrustes: rmse 0.06980143 max resid 0.1054105
Run 20 stress 0
... Procrustes: rmse 0.1279696 max resid 0.2320436
Run 21 stress 0
... Procrustes: rmse 0.04817071 max resid 0.07572467
Run 22 stress 0.2672328
Run 23 stress 0
... Procrustes: rmse 0.1481909 max resid 0.236068
Run 24 stress 0
... Procrustes: rmse 0.1613522 max resid 0.1946558
Run 25 stress 9.94811e-05
... Procrustes: rmse 0.1824641 max resid 0.2358877
Run 26 stress 0
... Procrustes: rmse 0.1727518 max resid 0.3489211
Run 27 stress 0
... Procrustes: rmse 0.1742554 max resid 0.2247009
Run 28 stress 0
... Procrustes: rmse 0.05942961 max resid 0.08747323
Run 29 stress 0
... Procrustes: rmse 0.1120925 max resid 0.1841444
Run 30 stress 3.903664e-05
... Procrustes: rmse 0.1325154 max resid 0.1912761
Run 31 stress 5.399369e-05
... Procrustes: rmse 0.1010359 max resid 0.1467465
Run 32 stress 7.379184e-05
... Procrustes: rmse 0.1331372 max resid 0.1950281
Run 33 stress 0
... Procrustes: rmse 0.1596573 max resid 0.2780751
Run 34 stress 0
... Procrustes: rmse 0.1545245 max resid 0.3209774
Run 35 stress 6.920823e-05
... Procrustes: rmse 0.112497 max resid 0.2057306
Run 36 stress 0
... Procrustes: rmse 0.1150425 max resid 0.1881103
Run 37 stress 0.1967694
Run 38 stress 0
... Procrustes: rmse 0.05315167 max resid 0.1032802
Run 39 stress 1.655269e-05
... Procrustes: rmse 0.1448603 max resid 0.2697769
Run 40 stress 0
... Procrustes: rmse 0.1612889 max resid 0.1910241
Run 41 stress 2.114832e-09
... Procrustes: rmse 0.1841392 max resid 0.2444196
Run 42 stress 0
... Procrustes: rmse 0.1425155 max resid 0.2249561
Run 43 stress 0.1967694
Run 44 stress 0
... Procrustes: rmse 0.1768091 max resid 0.2551034
Run 45 stress 0
... Procrustes: rmse 0.1698371 max resid 0.3456138
Run 46 stress 0
... Procrustes: rmse 0.1529447 max resid 0.2827875
Run 47 stress 0.2181459
Run 48 stress 0
... Procrustes: rmse 0.05269924 max resid 0.08362616
Run 49 stress 0
... Procrustes: rmse 0.1547511 max resid 0.2465553
Run 50 stress 0.2672326
*** No convergence -- monoMDS stopping criteria:
43: stress < smin
6: stress ratio > sratmax
1: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
[[1]]
[[1]][[1]]
[[1]][[1]]$x
[1] 0.455630579 0.031687447 0.011165044 0.467932180 0.456814980 0.481128614 0.448182873
[8] 0.019509172 0.009092947 0.039385854 0.493691249 0.482584407 0.460410026 0.449150152
[15] 0.023656561
[[1]][[1]]$y
[1] 6.517246e-01 4.849436e-01 8.138855e-02 6.736325e-01 6.517246e-01 1.128445e+00
[7] 5.893142e-01 4.222970e-01 4.941636e-13 5.393635e-01 1.135606e+00 1.128445e+00
[13] 6.568494e-01 5.893142e-01 4.222970e-01
[[1]][[1]]$yf
[1] 6.517246e-01 4.849436e-01 8.138855e-02 6.736325e-01 6.517246e-01 1.128445e+00
[7] 5.893142e-01 4.222970e-01 4.941636e-13 5.393635e-01 1.135606e+00 1.128445e+00
[13] 6.568494e-01 5.893142e-01 4.222970e-01
[[1]][[2]]
[[2]]
[[2]][[1]]
[[2]][[1]]$x
[1] 0.020370742 0.021775514 0.536277585 0.527567678 0.542073274 0.005729939 0.529162348
[8] 0.520405651 0.535017731 0.527317058 0.518564217 0.533141106 0.010204119 0.007195109
[15] 0.015121781
[[2]][[1]]$y
[1] 0.04039450 0.04312173 1.07137806 1.05398223 1.08296778 0.00398703 1.05716121 1.03967373
[9] 1.06883600 1.05345375 1.03596315 1.06513158 0.01763126 0.01188034 0.02949857
[[2]][[1]]$yf
[1] 0.04039450 0.04312173 1.07137806 1.05398223 1.08296778 0.00398703 1.05716121 1.03967373
[9] 1.06883600 1.05345375 1.03596315 1.06513158 0.01763126 0.01188034 0.02949857
[[2]][[2]]
[[3]]
[[3]][[1]]
[[3]][[1]]$x
[1] 0.019042371 0.023817717 0.503298143 0.495909258 0.498116609 0.009214567 0.485746569
[8] 0.478316420 0.480640216 0.484014906 0.476718469 0.478965566 0.008037243 0.006246801
[15] 0.005830554
[[3]][[1]]$y
[1] 0.036924981 0.047630271 1.005825904 0.990987984 0.995474004 0.016688333 0.970703879
[8] 0.955938452 0.960452131 0.967299934 0.952643143 0.957195285 0.016039401 0.013371015
[15] 0.005060069
[[3]][[1]]$yf
[1] 0.036924981 0.047630271 1.005825904 0.990987984 0.995474004 0.016688333 0.970703879
[8] 0.955938452 0.960452131 0.967299934 0.952643143 0.957195285 0.016039401 0.013371015
[15] 0.005060069
[[3]][[2]]
[[4]]
[[4]][[1]]
[[4]][[1]]$x
[1] 0.05744362 0.62639996 0.61813302 0.16695428 0.61361505 0.60941196 0.60119243 0.12118886
[9] 0.59666234 0.01744839 0.52985464 0.01718967 0.52315263 0.01498784 0.51574041
[[4]][[1]]$y
[1] 0.34509345 1.53479093 1.36136022 0.84278066 1.34760283 1.22941516 1.09635209 0.50452276
[9] 1.08129922 0.31275289 0.95182549 0.31233944 0.95146566 0.01613203 0.93543738
[[4]][[1]]$yf
[1] 0.34509345 1.53479093 1.36136022 0.84278066 1.34760283 1.22941516 1.09635209 0.50452276
[9] 1.08129922 0.31275289 0.95182549 0.31233944 0.95146566 0.01613203 0.93543738
[[4]][[2]]
Filler 1
# lapply the function with all four loci
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F1_0")'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0
Run 1 stress 0
... Procrustes: rmse 0.08306481 max resid 0.1500837
Run 2 stress 8.393474e-05
... Procrustes: rmse 0.0650365 max resid 0.1016619
Run 3 stress 9.601771e-05
... Procrustes: rmse 0.1508988 max resid 0.2618136
Run 4 stress 0
... Procrustes: rmse 0.09565963 max resid 0.1719531
Run 5 stress 9.762288e-05
... Procrustes: rmse 0.1508948 max resid 0.2618009
Run 6 stress 0
... Procrustes: rmse 0.05516981 max resid 0.08298733
Run 7 stress 0.005052253
Run 8 stress 9.545577e-05
... Procrustes: rmse 0.1508969 max resid 0.2618038
Run 9 stress 6.304464e-05
... Procrustes: rmse 0.06759018 max resid 0.1260851
Run 10 stress 0
... Procrustes: rmse 0.06590909 max resid 0.1190405
Run 11 stress 3.912468e-05
... Procrustes: rmse 0.07810179 max resid 0.1189405
Run 12 stress 0.1964001
Run 13 stress 0
... Procrustes: rmse 0.05915666 max resid 0.1147621
Run 14 stress 1.210162e-05
... Procrustes: rmse 0.1275159 max resid 0.2214862
Run 15 stress 0.1964001
Run 16 stress 7.15227e-05
... Procrustes: rmse 0.06823941 max resid 0.09896617
Run 17 stress 7.930743e-05
... Procrustes: rmse 0.1418135 max resid 0.2454811
Run 18 stress 0.2181459
Run 19 stress 0.004773638
Run 20 stress 0
... Procrustes: rmse 0.05536676 max resid 0.1114028
Run 21 stress 0
... Procrustes: rmse 0.05288896 max resid 0.07930563
Run 22 stress 0
... Procrustes: rmse 0.07216272 max resid 0.1259475
Run 23 stress 0
... Procrustes: rmse 0.09203779 max resid 0.1867488
Run 24 stress 9.716907e-05
... Procrustes: rmse 0.1509008 max resid 0.2618182
Run 25 stress 0
... Procrustes: rmse 0.05973701 max resid 0.09446323
Run 26 stress 0
... Procrustes: rmse 0.08943409 max resid 0.1756147
Run 27 stress 0
... Procrustes: rmse 0.0844327 max resid 0.14415
Run 28 stress 0
... Procrustes: rmse 0.1119332 max resid 0.2013292
Run 29 stress 1.980986e-05
... Procrustes: rmse 0.06570038 max resid 0.1169952
Run 30 stress 0.2761354
Run 31 stress 0
... Procrustes: rmse 0.07770718 max resid 0.1074483
Run 32 stress 0
... Procrustes: rmse 0.06254715 max resid 0.09887051
Run 33 stress 0
... Procrustes: rmse 0.04758941 max resid 0.06036237
Run 34 stress 0.1964001
Run 35 stress 0.1964001
Run 36 stress 0.1964001
Run 37 stress 0.2761343
Run 38 stress 0
... Procrustes: rmse 0.05251142 max resid 0.0876186
Run 39 stress 0.004699463
Run 40 stress 0.1964001
Run 41 stress 0
... Procrustes: rmse 0.1306747 max resid 0.2260125
Run 42 stress 0
... Procrustes: rmse 0.09506064 max resid 0.1884457
Run 43 stress 0
... Procrustes: rmse 0.07195941 max resid 0.1353737
Run 44 stress 9.83279e-05
... Procrustes: rmse 0.1509008 max resid 0.261823
Run 45 stress 0
... Procrustes: rmse 0.06384242 max resid 0.1159743
Run 46 stress 3.978645e-05
... Procrustes: rmse 0.06397951 max resid 0.08485267
Run 47 stress 9.82312e-05
... Procrustes: rmse 0.150878 max resid 0.2617063
Run 48 stress 8.665296e-05
... Procrustes: rmse 0.08506109 max resid 0.1365683
Run 49 stress 9.70663e-05
... Procrustes: rmse 0.1508964 max resid 0.2618059
Run 50 stress 9.490215e-05
... Procrustes: rmse 0.1179302 max resid 0.2025423
*** No convergence -- monoMDS stopping criteria:
3: no. of iterations >= maxit
38: stress < smin
8: stress ratio > sratmax
1: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient dataRun 0 stress 0
Run 1 stress 9.84425e-05
... Procrustes: rmse 0.05796188 max resid 0.09363277
Run 2 stress 9.192271e-05
... Procrustes: rmse 0.05793028 max resid 0.09358927
Run 3 stress 0.04733294
Run 4 stress 9.141673e-05
... Procrustes: rmse 0.08849435 max resid 0.1345743
Run 5 stress 0.1967694
Run 6 stress 9.290967e-05
... Procrustes: rmse 0.05792952 max resid 0.09358641
Run 7 stress 0.04733513
Run 8 stress 0.07049135
Run 9 stress 9.76217e-05
... Procrustes: rmse 0.03149038 max resid 0.04717506
Run 10 stress 0
... Procrustes: rmse 0.07465643 max resid 0.1020887
Run 11 stress 0.2006011
Run 12 stress 0
... Procrustes: rmse 0.05781496 max resid 0.07364721
Run 13 stress 0
... Procrustes: rmse 0.08604689 max resid 0.1313007
Run 14 stress 0.04733473
Run 15 stress 0
... Procrustes: rmse 0.02333959 max resid 0.0360221
Run 16 stress 0.04733407
Run 17 stress 0.04733319
Run 18 stress 0.2006012
Run 19 stress 0.04733288
Run 20 stress 0.2181459
Run 21 stress 0
... Procrustes: rmse 0.008697031 max resid 0.01219564
Run 22 stress 0.07049114
Run 23 stress 0.04733426
Run 24 stress 0.2181459
Run 25 stress 0
... Procrustes: rmse 0.1113266 max resid 0.175125
Run 26 stress 0.2006012
Run 27 stress 9.16307e-05
... Procrustes: rmse 0.03452735 max resid 0.05457119
Run 28 stress 0
... Procrustes: rmse 0.08900288 max resid 0.1360343
Run 29 stress 0.1967694
Run 30 stress 9.805726e-05
... Procrustes: rmse 0.0245335 max resid 0.04198703
Run 31 stress 0
... Procrustes: rmse 0.07694084 max resid 0.1091291
Run 32 stress 0.07048915
Run 33 stress 0.04733381
Run 34 stress 0.04733334
Run 35 stress 0.1620083
Run 36 stress 0.2269842
Run 37 stress 0.2006011
Run 38 stress 0.1967694
Run 39 stress 0
... Procrustes: rmse 0.05828024 max resid 0.08635312
Run 40 stress 0.07049115
Run 41 stress 0.2269841
Run 42 stress 9.327995e-05
... Procrustes: rmse 0.04306195 max resid 0.07538903
Run 43 stress 0
... Procrustes: rmse 0.05405981 max resid 0.08557559
Run 44 stress 0.04733354
Run 45 stress 9.840137e-05
... Procrustes: rmse 0.05797403 max resid 0.09365241
Run 46 stress 0.047335
Run 47 stress 0
... Procrustes: rmse 0.03770873 max resid 0.05329281
Run 48 stress 9.996972e-05
... Procrustes: rmse 0.0579823 max resid 0.09366323
Run 49 stress 9.794911e-05
... Procrustes: rmse 0.02794148 max resid 0.044609
Run 50 stress 9.76326e-05
... Procrustes: rmse 0.05796927 max resid 0.0936448
*** No convergence -- monoMDS stopping criteria:
23: stress < smin
22: stress ratio > sratmax
5: scale factor of the gradient < sfgrmin
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 9.288062e-05
Run 1 stress 9.935409e-05
... Procrustes: rmse 0.01927745 max resid 0.03054252
Run 2 stress 0.2672326
Run 3 stress 9.494183e-05
... Procrustes: rmse 0.02136947 max resid 0.03540566
Run 4 stress 0.0001672882
... Procrustes: rmse 0.2998376 max resid 0.435781
Run 5 stress 0.0002487257
... Procrustes: rmse 0.2995368 max resid 0.4357041
Run 6 stress 0.1970932
Run 7 stress 9.862627e-05
... Procrustes: rmse 0.0709689 max resid 0.08719569
Run 8 stress 8.325195e-05
... New best solution
... Procrustes: rmse 0.1037393 max resid 0.1665368
Run 9 stress 0.1967694
Run 10 stress 0.1967694
Run 11 stress 9.980822e-05
... Procrustes: rmse 0.2982585 max resid 0.4416807
Run 12 stress 9.671238e-05
... Procrustes: rmse 0.03270725 max resid 0.05584652
Run 13 stress 8.259271e-05
... New best solution
... Procrustes: rmse 0.04375163 max resid 0.06171771
Run 14 stress 9.865693e-05
... Procrustes: rmse 0.3097449 max resid 0.4613929
Run 15 stress 0.2761338
Run 16 stress 9.417506e-05
... Procrustes: rmse 0.09823271 max resid 0.1392558
Run 17 stress 0.0002334998
... Procrustes: rmse 0.3092202 max resid 0.461155
Run 18 stress 0.0002002533
... Procrustes: rmse 0.3093261 max resid 0.461202
Run 19 stress 9.77665e-05
... Procrustes: rmse 0.09650949 max resid 0.1341966
Run 20 stress 0.0001700821
... Procrustes: rmse 0.3094269 max resid 0.4612464
Run 21 stress 8.1437e-05
... New best solution
... Procrustes: rmse 0.3097858 max resid 0.4613998
Run 22 stress 0
... New best solution
... Procrustes: rmse 0.3070755 max resid 0.5145376
Run 23 stress 8.904473e-05
... Procrustes: rmse 0.1163195 max resid 0.1895859
Run 24 stress 0.0001143681
... Procrustes: rmse 0.306872 max resid 0.4426481
Run 25 stress 9.571803e-05
... Procrustes: rmse 0.06819767 max resid 0.1180618
Run 26 stress 0.0002413045
... Procrustes: rmse 0.3064116 max resid 0.4425511
Run 27 stress 9.465741e-05
... Procrustes: rmse 0.3070461 max resid 0.4428306
Run 28 stress 0.1967694
Run 29 stress 0.2761345
Run 30 stress 0.2162637
Run 31 stress 7.392473e-05
... Procrustes: rmse 0.1293841 max resid 0.2123618
Run 32 stress 0.0002153372
... Procrustes: rmse 0.3065 max resid 0.4425697
Run 33 stress 0.1967694
Run 34 stress 9.997403e-05
... Procrustes: rmse 0.3068736 max resid 0.4426477
Run 35 stress 9.653248e-05
... Procrustes: rmse 0.130319 max resid 0.2185764
Run 36 stress 9.932296e-05
... Procrustes: rmse 0.1306913 max resid 0.2187037
Run 37 stress 8.652972e-05
... Procrustes: rmse 0.3070574 max resid 0.442818
Run 38 stress 9.816365e-05
... Procrustes: rmse 0.1303265 max resid 0.2185714
Run 39 stress 0.0001534673
... Procrustes: rmse 0.306696 max resid 0.4426111
Run 40 stress 0.0002483038
... Procrustes: rmse 0.3063834 max resid 0.4425453
Run 41 stress 9.600741e-05
... Procrustes: rmse 0.3070428 max resid 0.442832
Run 42 stress 0.1967694
Run 43 stress 0.0001062605
... Procrustes: rmse 0.3068523 max resid 0.4426432
Run 44 stress 6.707316e-05
... Procrustes: rmse 0.06881967 max resid 0.119313
Run 45 stress 9.543737e-05
... Procrustes: rmse 0.3070428 max resid 0.4428315
Run 46 stress 0.1967694
Run 47 stress 9.08907e-05
... Procrustes: rmse 0.3070547 max resid 0.4428253
Run 48 stress 0.2269841
Run 49 stress 8.98486e-05
... Procrustes: rmse 0.03419239 max resid 0.06447473
Run 50 stress 9.330837e-05
... Procrustes: rmse 0.0531996 max resid 0.08947516
*** No convergence -- monoMDS stopping criteria:
11: no. of iterations >= maxit
27: stress < smin
10: stress ratio > sratmax
2: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0.0004669906
Run 1 stress 0.276134
Run 2 stress 9.492375e-05
... New best solution
... Procrustes: rmse 0.1094817 max resid 0.1950747
Run 3 stress 0.1420473
Run 4 stress 0.001917046
Run 5 stress 0.001346452
Run 6 stress 9.832348e-05
... Procrustes: rmse 0.1093233 max resid 0.2191431
Run 7 stress 0.005107424
Run 8 stress 0.001539236
Run 9 stress 9.799846e-05
... Procrustes: rmse 0.0174658 max resid 0.03175275
Run 10 stress 9.858956e-05
... Procrustes: rmse 0.05003124 max resid 0.09455763
Run 11 stress 9.671445e-05
... Procrustes: rmse 0.05685749 max resid 0.09948346
Run 12 stress 0.202923
Run 13 stress 0.0002418935
... Procrustes: rmse 0.1127767 max resid 0.2187444
Run 14 stress 0.001909211
Run 15 stress 0.2159914
Run 16 stress 0.0007357664
Run 17 stress 0.002043995
Run 18 stress 0.001562868
Run 19 stress 0.001774865
Run 20 stress 9.976101e-05
... Procrustes: rmse 0.03926445 max resid 0.07461661
Run 21 stress 0.002192585
Run 22 stress 0.002294747
Run 23 stress 9.899158e-05
... Procrustes: rmse 0.1331164 max resid 0.2750697
Run 24 stress 0.002191628
Run 25 stress 9.123e-05
... New best solution
... Procrustes: rmse 0.03559199 max resid 0.06037134
Run 26 stress 9.942274e-05
... Procrustes: rmse 0.2224874 max resid 0.4728508
Run 27 stress 0.2181459
Run 28 stress 9.723968e-05
... Procrustes: rmse 0.02377437 max resid 0.0392155
Run 29 stress 9.611648e-05
... Procrustes: rmse 0.08741644 max resid 0.1686624
Run 30 stress 0.001136239
Run 31 stress 0.0003363814
... Procrustes: rmse 0.2532811 max resid 0.4645238
Run 32 stress 0.0007536452
Run 33 stress 9.870571e-05
... Procrustes: rmse 0.07105116 max resid 0.1356258
Run 34 stress 0.001865427
Run 35 stress 0.1420473
Run 36 stress 0.001677937
Run 37 stress 9.635011e-05
... Procrustes: rmse 0.02560996 max resid 0.04562696
Run 38 stress 9.880459e-05
... Procrustes: rmse 0.0426541 max resid 0.07618848
Run 39 stress 0.002961839
Run 40 stress 0.202923
Run 41 stress 0.0004419442
... Procrustes: rmse 0.1508734 max resid 0.2778805
Run 42 stress 0.0007780449
Run 43 stress 0.001607698
Run 44 stress 9.535506e-05
... Procrustes: rmse 0.07076021 max resid 0.1304869
Run 45 stress 0.003001466
Run 46 stress 0.001432106
Run 47 stress 9.871739e-05
... Procrustes: rmse 0.1106065 max resid 0.2158964
Run 48 stress 0.00114001
Run 49 stress 0.2181459
Run 50 stress 0.1420473
*** No convergence -- monoMDS stopping criteria:
25: no. of iterations >= maxit
16: stress < smin
5: stress ratio > sratmax
4: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient dataskipping half-change scaling: too few points below threshold
[[1]]
[[1]][[1]]
[[1]][[1]]$x
[1] 0.08235372 0.16922326 0.13399688 0.18851684 0.11184361 0.16554871 0.11063268 0.18048591
[9] 0.10709883 0.16577753 0.16120027 0.14635979 0.20065758 0.14547214 0.16433578
[[1]][[1]]$y
[1] 0.03627004 0.20772785 0.08460255 0.22588311 0.06460388 0.17297458 0.06400572 0.20985487
[9] 0.04354659 0.18040217 0.16482254 0.15153341 0.25976659 0.10136720 0.16645123
[[1]][[1]]$yf
[1] 0.03627004 0.20772785 0.08460255 0.22588311 0.06460388 0.17297458 0.06400572 0.20985487
[9] 0.04354659 0.18040217 0.16482254 0.15153341 0.25976659 0.10136720 0.16645123
[[1]][[2]]
[[2]]
[[2]][[1]]
[[2]][[1]]$x
[1] 0.17188052 0.11550327 0.16185618 0.11945687 0.14165637 0.15403252 0.12319280 0.12714596
[9] 0.19578535 0.17324048 0.09524785 0.11246285 0.15984646 0.23541141 0.15059186
[[2]][[1]]$y
[1] 0.23019707 0.10139864 0.22679648 0.11216333 0.15433111 0.18730854 0.13309154 0.13309220
[9] 0.28206999 0.24306098 0.05764288 0.09951253 0.18843030 0.34128563 0.15699641
[[2]][[1]]$yf
[1] 0.23019707 0.10139864 0.22679648 0.11216333 0.15433111 0.18730854 0.13309154 0.13309220
[9] 0.28206999 0.24306098 0.05764288 0.09951253 0.18843030 0.34128563 0.15699641
[[2]][[2]]
[[3]]
[[3]][[1]]
[[3]][[1]]$x
[1] 0.08654040 0.09198057 0.14673668 0.12506019 0.12096826 0.08199491 0.11060969 0.09325268
[9] 0.09229807 0.12407869 0.08475139 0.08816411 0.06447852 0.06233777 0.02992395
[[3]][[1]]$y
[1] 0.10685122 0.11149657 0.25419486 0.19916463 0.19567153 0.10664459 0.15099400 0.12713997
[9] 0.11877822 0.19580864 0.10670595 0.10846333 0.10649818 0.09756169 0.01163680
[[3]][[1]]$yf
[1] 0.10685122 0.11149657 0.25419486 0.19916463 0.19567153 0.10664459 0.15099400 0.12713997
[9] 0.11877822 0.19580864 0.10670595 0.10846333 0.10649818 0.09756169 0.01163680
[[3]][[2]]
[[4]]
[[4]][[1]]
[[4]][[1]]$x
[1] 0.9352071 0.8379840 0.2250675 0.2428324 0.2359233 0.3431216 0.9032836 0.9496608 0.9455931
[10] 0.8005636 0.8799819 0.8581374 0.1168503 0.1276983 0.1453950
[[4]][[1]]$y
[1] 0.9486908259 0.8976707534 0.0008126971 0.0012892839 0.0006862254 0.8296463185
[7] 0.9487862071 0.9496608466 0.9492865248 0.8970804407 0.8975695046 0.8977535755
[13] 0.0008756401 0.0006841696 0.0006316081
[[4]][[1]]$yf
[1] 0.9487385165 0.8976646112 0.0007494612 0.0012892839 0.0007494612 0.8296463185
[7] 0.9487385165 0.9496608466 0.9492865248 0.8970804407 0.8976646112 0.8976646112
[13] 0.0007304726 0.0007304726 0.0007304726
[[4]][[2]]
# analyze sample F1_10
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F1_10")'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0
Run 1 stress 0.2761359
Run 2 stress 0
... Procrustes: rmse 0.1782514 max resid 0.3041158
Run 3 stress 0.276135
Run 4 stress 7.66081e-05
... Procrustes: rmse 0.2344818 max resid 0.3668063
Run 5 stress 5.015669e-05
... Procrustes: rmse 0.05616418 max resid 0.1050068
Run 6 stress 0
... Procrustes: rmse 0.04417714 max resid 0.07079991
Run 7 stress 0.0008303259
Run 8 stress 0.1420473
Run 9 stress 0.001924173
Run 10 stress 9.684896e-05
... Procrustes: rmse 0.2523453 max resid 0.3905902
Run 11 stress 0
... Procrustes: rmse 0.171424 max resid 0.2945327
Run 12 stress 0.1420473
Run 13 stress 7.792994e-05
... Procrustes: rmse 0.219393 max resid 0.3191929
Run 14 stress 0.1967694
Run 15 stress 8.119786e-05
... Procrustes: rmse 0.05195433 max resid 0.08525008
Run 16 stress 0
... Procrustes: rmse 0.1510171 max resid 0.2482222
Run 17 stress 0
... Procrustes: rmse 0.2236458 max resid 0.3640034
Run 18 stress 0.1420473
Run 19 stress 0.0004255954
... Procrustes: rmse 0.238965 max resid 0.3759676
Run 20 stress 0
... Procrustes: rmse 0.1010101 max resid 0.178044
Run 21 stress 8.971604e-05
... Procrustes: rmse 0.2374718 max resid 0.3491048
Run 22 stress 0
... Procrustes: rmse 0.1874702 max resid 0.318879
Run 23 stress 9.277236e-06
... Procrustes: rmse 0.2201936 max resid 0.2971146
Run 24 stress 6.871951e-05
... Procrustes: rmse 0.07134347 max resid 0.1072856
Run 25 stress 0
... Procrustes: rmse 0.2160544 max resid 0.3267475
Run 26 stress 9.537637e-05
... Procrustes: rmse 0.2602977 max resid 0.3376999
Run 27 stress 0
... Procrustes: rmse 0.1732755 max resid 0.2359424
Run 28 stress 0
... Procrustes: rmse 0.2110359 max resid 0.3146671
Run 29 stress 9.737606e-05
... Procrustes: rmse 0.06191323 max resid 0.1033323
Run 30 stress 0
... Procrustes: rmse 0.2311834 max resid 0.3309575
Run 31 stress 0
... Procrustes: rmse 0.2523026 max resid 0.3825019
Run 32 stress 6.439155e-05
... Procrustes: rmse 0.2673991 max resid 0.3576358
Run 33 stress 0
... Procrustes: rmse 0.1757631 max resid 0.3031776
Run 34 stress 9.249799e-05
... Procrustes: rmse 0.2105243 max resid 0.3388449
Run 35 stress 8.411932e-05
... Procrustes: rmse 0.06075886 max resid 0.1010699
Run 36 stress 0
... Procrustes: rmse 0.06367534 max resid 0.1162029
Run 37 stress 9.307845e-05
... Procrustes: rmse 0.1873243 max resid 0.3223229
Run 38 stress 2.944851e-06
... Procrustes: rmse 0.04917603 max resid 0.07568098
Run 39 stress 0.0001724431
... Procrustes: rmse 0.2495602 max resid 0.3833985
Run 40 stress 0.1420473
Run 41 stress 0
... Procrustes: rmse 0.1569581 max resid 0.2203168
Run 42 stress 9.971866e-05
... Procrustes: rmse 0.2580352 max resid 0.3930695
Run 43 stress 0
... Procrustes: rmse 0.1520931 max resid 0.2414392
Run 44 stress 0.1420473
Run 45 stress 0
... Procrustes: rmse 0.175364 max resid 0.3003158
Run 46 stress 0
... Procrustes: rmse 0.1453873 max resid 0.2099739
Run 47 stress 9.167412e-05
... Procrustes: rmse 0.1879636 max resid 0.2853971
Run 48 stress 7.957956e-05
... Procrustes: rmse 0.03438225 max resid 0.06038609
Run 49 stress 0
... Procrustes: rmse 0.1340051 max resid 0.2204579
Run 50 stress 0
... Procrustes: rmse 0.2149505 max resid 0.2891899
*** No convergence -- monoMDS stopping criteria:
4: no. of iterations >= maxit
38: stress < smin
4: stress ratio > sratmax
4: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient dataRun 0 stress 0
Run 1 stress 0.2181459
Run 2 stress 0.2672326
Run 3 stress 0
... Procrustes: rmse 0.1706527 max resid 0.2472902
Run 4 stress 0
... Procrustes: rmse 0.163524 max resid 0.2407503
Run 5 stress 0.2143661
Run 6 stress 0
... Procrustes: rmse 0.192329 max resid 0.2568778
Run 7 stress 0
... Procrustes: rmse 0.1772413 max resid 0.2274027
Run 8 stress 9.518459e-05
... Procrustes: rmse 0.2217095 max resid 0.3941604
Run 9 stress 0.1470313
Run 10 stress 0.276137
Run 11 stress 0
... Procrustes: rmse 0.1622381 max resid 0.2364686
Run 12 stress 0
... Procrustes: rmse 0.1314665 max resid 0.1818057
Run 13 stress 0
... Procrustes: rmse 0.1467775 max resid 0.1923097
Run 14 stress 6.216642e-05
... Procrustes: rmse 0.2193254 max resid 0.3901086
Run 15 stress 0
... Procrustes: rmse 0.1293252 max resid 0.2350656
Run 16 stress 9.635216e-05
... Procrustes: rmse 0.170729 max resid 0.284366
Run 17 stress 0
... Procrustes: rmse 0.1651769 max resid 0.2469877
Run 18 stress 3.241269e-05
... Procrustes: rmse 0.1437676 max resid 0.1771954
Run 19 stress 0
... Procrustes: rmse 0.1572113 max resid 0.1960299
Run 20 stress 0
... Procrustes: rmse 0.1458955 max resid 0.2260915
Run 21 stress 0.1470313
Run 22 stress 0
... Procrustes: rmse 0.1599721 max resid 0.2298241
Run 23 stress 0.1470313
Run 24 stress 9.5205e-05
... Procrustes: rmse 0.1277011 max resid 0.2292867
Run 25 stress 0.2143661
Run 26 stress 0.2181459
Run 27 stress 0
... Procrustes: rmse 0.1988506 max resid 0.3485836
Run 28 stress 0.2761346
Run 29 stress 0
... Procrustes: rmse 0.1389998 max resid 0.1856175
Run 30 stress 0
... Procrustes: rmse 0.1415347 max resid 0.2085836
Run 31 stress 0
... Procrustes: rmse 0.08130354 max resid 0.133839
Run 32 stress 0
... Procrustes: rmse 0.160239 max resid 0.268081
Run 33 stress 0.2761341
Run 34 stress 0
... Procrustes: rmse 0.1419449 max resid 0.1854868
Run 35 stress 0
... Procrustes: rmse 0.09363463 max resid 0.124746
Run 36 stress 0.2143661
Run 37 stress 0
... Procrustes: rmse 0.1472885 max resid 0.1981838
Run 38 stress 0
... Procrustes: rmse 0.1518164 max resid 0.2216571
Run 39 stress 0
... Procrustes: rmse 0.1489788 max resid 0.2078257
Run 40 stress 9.060784e-05
... Procrustes: rmse 0.1442697 max resid 0.2085266
Run 41 stress 8.595739e-05
... Procrustes: rmse 0.177837 max resid 0.213702
Run 42 stress 5.485652e-05
... Procrustes: rmse 0.2004741 max resid 0.2437479
Run 43 stress 0
... Procrustes: rmse 0.145018 max resid 0.1997814
Run 44 stress 0
... Procrustes: rmse 0.1634364 max resid 0.2310717
Run 45 stress 9.019576e-05
... Procrustes: rmse 0.1633155 max resid 0.2125998
Run 46 stress 0
... Procrustes: rmse 0.2018461 max resid 0.2491446
Run 47 stress 0
... Procrustes: rmse 0.1633162 max resid 0.2392174
Run 48 stress 0
... Procrustes: rmse 0.1735698 max resid 0.2286069
Run 49 stress 0.2280378
Run 50 stress 0
... Procrustes: rmse 0.1229451 max resid 0.1979459
*** No convergence -- monoMDS stopping criteria:
37: stress < smin
8: stress ratio > sratmax
5: scale factor of the gradient < sfgrmin
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 8.331396e-05
Run 1 stress 0.2143661
Run 2 stress 0.2269841
Run 3 stress 6.487667e-05
... New best solution
... Procrustes: rmse 0.08939749 max resid 0.1124175
Run 4 stress 9.788692e-05
... Procrustes: rmse 0.09199573 max resid 0.1982159
Run 5 stress 9.371137e-05
... Procrustes: rmse 0.1791542 max resid 0.2720575
Run 6 stress 0.2269841
Run 7 stress 7.936958e-05
... Procrustes: rmse 0.09203145 max resid 0.1913289
Run 8 stress 8.831308e-05
... Procrustes: rmse 0.2081254 max resid 0.3341102
Run 9 stress 0.2680414
Run 10 stress 9.919535e-05
... Procrustes: rmse 0.09120056 max resid 0.1948101
Run 11 stress 8.86129e-05
... Procrustes: rmse 0.1834003 max resid 0.2830286
Run 12 stress 0
... New best solution
... Procrustes: rmse 0.1773722 max resid 0.2883808
Run 13 stress 9.900996e-05
... Procrustes: rmse 0.07657583 max resid 0.1235298
Run 14 stress 0
... Procrustes: rmse 0.1853513 max resid 0.3387926
Run 15 stress 9.021276e-05
... Procrustes: rmse 0.06294959 max resid 0.07786125
Run 16 stress 1.330608e-06
... Procrustes: rmse 0.1460244 max resid 0.2544885
Run 17 stress 9.19803e-05
... Procrustes: rmse 0.07257653 max resid 0.1008073
Run 18 stress 0
... Procrustes: rmse 0.1464016 max resid 0.2525666
Run 19 stress 8.532005e-05
... Procrustes: rmse 0.1496782 max resid 0.2761175
Run 20 stress 0
... Procrustes: rmse 0.1795446 max resid 0.3025875
Run 21 stress 0.2269841
Run 22 stress 0
... Procrustes: rmse 0.08708322 max resid 0.1248659
Run 23 stress 0
... Procrustes: rmse 0.06197543 max resid 0.09957178
Run 24 stress 8.35669e-05
... Procrustes: rmse 0.170691 max resid 0.2848675
Run 25 stress 0
... Procrustes: rmse 0.04580526 max resid 0.07458275
Run 26 stress 0.2673072
Run 27 stress 0
... Procrustes: rmse 0.1324469 max resid 0.262764
Run 28 stress 9.937059e-05
... Procrustes: rmse 0.13472 max resid 0.2629956
Run 29 stress 0.2143661
Run 30 stress 0.2269841
Run 31 stress 0
... Procrustes: rmse 0.06346933 max resid 0.1089092
Run 32 stress 0
... Procrustes: rmse 0.1805905 max resid 0.3533108
Run 33 stress 0.2269849
Run 34 stress 6.446752e-05
... Procrustes: rmse 0.05841119 max resid 0.1013562
Run 35 stress 4.001065e-05
... Procrustes: rmse 0.05248738 max resid 0.080618
Run 36 stress 9.288712e-05
... Procrustes: rmse 0.06909143 max resid 0.08507696
Run 37 stress 0
... Procrustes: rmse 0.160548 max resid 0.2066414
Run 38 stress 0.2143661
Run 39 stress 8.740246e-05
... Procrustes: rmse 0.156384 max resid 0.2623792
Run 40 stress 0
... Procrustes: rmse 0.08440196 max resid 0.1253443
Run 41 stress 0
... Procrustes: rmse 0.1668615 max resid 0.2776859
Run 42 stress 9.611301e-05
... Procrustes: rmse 0.08661236 max resid 0.1321455
Run 43 stress 9.462554e-05
... Procrustes: rmse 0.1493892 max resid 0.2782459
Run 44 stress 0
... Procrustes: rmse 0.1461522 max resid 0.2346507
Run 45 stress 0
... Procrustes: rmse 0.09250051 max resid 0.1396608
Run 46 stress 0
... Procrustes: rmse 0.09637135 max resid 0.1429823
Run 47 stress 0
... Procrustes: rmse 0.1224828 max resid 0.224107
Run 48 stress 0
... Procrustes: rmse 0.09505105 max resid 0.1433963
Run 49 stress 9.677961e-05
... Procrustes: rmse 0.149219 max resid 0.2381071
Run 50 stress 0
... Procrustes: rmse 0.1813549 max resid 0.297632
*** No convergence -- monoMDS stopping criteria:
40: stress < smin
6: stress ratio > sratmax
4: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0.0003381618
Run 1 stress 0.1557467
Run 2 stress 9.845105e-05
... New best solution
... Procrustes: rmse 0.1048254 max resid 0.1864059
Run 3 stress 9.19975e-05
... New best solution
... Procrustes: rmse 0.02914148 max resid 0.04312212
Run 4 stress 0.1557467
Run 5 stress 2.725825e-05
... New best solution
... Procrustes: rmse 0.03067527 max resid 0.04405784
Run 6 stress 0.2269841
Run 7 stress 0.0001049999
... Procrustes: rmse 0.15985 max resid 0.2605553
Run 8 stress 3.537058e-05
... Procrustes: rmse 0.2119709 max resid 0.4111482
Run 9 stress 0.2673072
Run 10 stress 0.0005377419
Run 11 stress 0.2405176
Run 12 stress 6.40736e-05
... Procrustes: rmse 0.2040217 max resid 0.401824
Run 13 stress 7.967293e-05
... Procrustes: rmse 0.2432073 max resid 0.4462463
Run 14 stress 8.66504e-05
... Procrustes: rmse 0.2432139 max resid 0.4462532
Run 15 stress 2.87675e-12
... New best solution
... Procrustes: rmse 0.2064273 max resid 0.4060603
Run 16 stress 9.567279e-05
... Procrustes: rmse 0.1090861 max resid 0.2245351
Run 17 stress 9.953336e-05
... Procrustes: rmse 0.212252 max resid 0.3884891
Run 18 stress 0.0005355913
Run 19 stress 9.724507e-05
... Procrustes: rmse 0.0983149 max resid 0.2151959
Run 20 stress 0.1557467
Run 21 stress 0.0003968548
... Procrustes: rmse 0.03623477 max resid 0.06199298
Run 22 stress 9.713423e-05
... Procrustes: rmse 0.05652115 max resid 0.07520578
Run 23 stress 0.2537798
Run 24 stress 8.14767e-05
... Procrustes: rmse 0.1563267 max resid 0.3098543
Run 25 stress 9.830106e-05
... Procrustes: rmse 0.05770281 max resid 0.07750037
Run 26 stress 0.2405176
Run 27 stress 8.950909e-05
... Procrustes: rmse 0.01268243 max resid 0.02263129
Run 28 stress 9.819799e-05
... Procrustes: rmse 0.2010997 max resid 0.3737692
Run 29 stress 0.1557467
Run 30 stress 9.675285e-05
... Procrustes: rmse 0.1825355 max resid 0.3463517
Run 31 stress 0.0002258919
... Procrustes: rmse 0.05028445 max resid 0.1036282
Run 32 stress 9.974731e-05
... Procrustes: rmse 0.0320083 max resid 0.04495142
Run 33 stress 0.276136
Run 34 stress 9.044341e-05
... Procrustes: rmse 0.03592827 max resid 0.04898544
Run 35 stress 1.006688e-05
... Procrustes: rmse 0.01824777 max resid 0.02534925
Run 36 stress 9.165075e-12
... Procrustes: rmse 0.009463528 max resid 0.01680075
Run 37 stress 0.2761343
Run 38 stress 0.1557467
Run 39 stress 8.299773e-05
... Procrustes: rmse 0.1537103 max resid 0.3094937
Run 40 stress 8.708343e-07
... Procrustes: rmse 0.004627223 max resid 0.0065775
... Similar to previous best
*** Solution reachedstress is (nearly) zero: you may have insufficient data
[[1]]
[[1]][[1]]
[[1]][[1]]$x
[1] 0.13698607 0.09966843 0.10699619 0.10826388 0.01901694 0.10159167 0.09617633 0.09417291
[9] 0.12464564 0.01529514 0.01598311 0.09191305 0.01019489 0.10127348 0.10211705
[[1]][[1]]$y
[1] 0.260924603 0.187600658 0.204085475 0.206342473 0.024133760 0.193181171 0.183124784
[8] 0.179406789 0.237279340 0.020018432 0.024130221 0.176253804 0.004544048 0.191386671
[15] 0.193227465
[[1]][[1]]$yf
[1] 0.260924603 0.187600658 0.204085475 0.206342473 0.024133760 0.193181171 0.183124784
[8] 0.179406789 0.237279340 0.020018432 0.024130221 0.176253804 0.004544048 0.191386671
[15] 0.193227465
[[1]][[2]]
[[2]]
[[2]][[1]]
[[2]][[1]]$x
[1] 0.011106264 0.010129804 0.103112045 0.105659264 0.112632087 0.008494632 0.107304247
[8] 0.110143755 0.117596064 0.104360784 0.106884229 0.114294945 0.010861103 0.012996968
[15] 0.011125930
[[2]][[1]]$y
[1] 0.014273185 0.007961262 0.200991368 0.204951528 0.222906835 0.007549471 0.206920319
[8] 0.211556266 0.229974438 0.201190970 0.205578041 0.223837116 0.011680656 0.029612355
[15] 0.019884124
[[2]][[1]]$yf
[1] 0.014273185 0.007961262 0.200991368 0.204951528 0.222906835 0.007549471 0.206920319
[8] 0.211556266 0.229974438 0.201190970 0.205578041 0.223837116 0.011680656 0.029612355
[15] 0.019884124
[[2]][[2]]
[[3]]
[[3]][[1]]
[[3]][[1]]$x
[1] 0.007792775 0.027989307 0.110738757 0.137087174 0.097747860 0.030930024 0.114437739
[8] 0.140806467 0.100411856 0.086553516 0.111921863 0.082383272 0.030778463 0.029183971
[15] 0.052960942
[[3]][[1]]$y
[1] 0.03395793 0.08266693 0.23131322 0.29755970 0.17818379 0.11542778 0.25502878 0.31162632
[9] 0.19400014 0.16553479 0.25297880 0.13859155 0.11203496 0.08370713 0.11952742
[[3]][[1]]$yf
[1] 0.03395793 0.08266693 0.23131322 0.29755970 0.17818379 0.11542778 0.25502878 0.31162632
[9] 0.19400014 0.16553479 0.25297880 0.13859155 0.11203496 0.08370713 0.11952742
[[3]][[2]]
[[4]]
[[4]][[1]]
[[4]][[1]]$x
[1] 0.02464158 0.13261474 0.18207265 0.25791915 0.21853437 0.13131404 0.18562618 0.26933494
[9] 0.21992670 0.11080077 0.18085388 0.15162022 0.10597322 0.09767366 0.18919575
[[4]][[1]]$y
[1] 2.301506e-11 1.982352e-01 3.516852e-01 4.626262e-01 4.022252e-01 1.982352e-01
[7] 3.516852e-01 4.626262e-01 4.022252e-01 1.872363e-01 3.504114e-01 2.048951e-01
[13] 1.852000e-01 1.848667e-01 3.596816e-01
[[4]][[1]]$yf
[1] 2.301506e-11 1.982352e-01 3.516852e-01 4.626262e-01 4.022252e-01 1.982352e-01
[7] 3.516852e-01 4.626262e-01 4.022252e-01 1.872363e-01 3.504114e-01 2.048951e-01
[13] 1.852000e-01 1.848667e-01 3.596816e-01
[[4]][[2]]
# analyze sample F1_2
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F1_2_")'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0
Run 1 stress 0
... Procrustes: rmse 0.1223218 max resid 0.1656872
Run 2 stress 7.072465e-05
... Procrustes: rmse 0.2508951 max resid 0.3448217
Run 3 stress 0
... Procrustes: rmse 0.0792535 max resid 0.1144785
Run 4 stress 0
... Procrustes: rmse 0.1722152 max resid 0.2352139
Run 5 stress 9.521242e-05
... Procrustes: rmse 0.2299656 max resid 0.3286579
Run 6 stress 2.267983e-05
... Procrustes: rmse 0.248738 max resid 0.3401303
Run 7 stress 2.594614e-05
... Procrustes: rmse 0.08292225 max resid 0.1291515
Run 8 stress 7.608151e-05
... Procrustes: rmse 0.2663827 max resid 0.3650842
Run 9 stress 0
... Procrustes: rmse 0.1848908 max resid 0.2604806
Run 10 stress 0
... Procrustes: rmse 0.1878001 max resid 0.2516045
Run 11 stress 7.076838e-05
... Procrustes: rmse 0.1984967 max resid 0.2830463
Run 12 stress 0
... Procrustes: rmse 0.2353047 max resid 0.3226877
Run 13 stress 9.295404e-05
... Procrustes: rmse 0.07587773 max resid 0.1020073
Run 14 stress 9.858445e-05
... Procrustes: rmse 0.1805886 max resid 0.2517475
Run 15 stress 9.609867e-05
... Procrustes: rmse 0.2663965 max resid 0.3650819
Run 16 stress 9.278764e-05
... Procrustes: rmse 0.2333222 max resid 0.3187146
Run 17 stress 9.99939e-05
... Procrustes: rmse 0.1785587 max resid 0.2294347
Run 18 stress 0
... Procrustes: rmse 0.1024366 max resid 0.1462867
Run 19 stress 0
... Procrustes: rmse 0.1157086 max resid 0.1819015
Run 20 stress 0
... Procrustes: rmse 0.1243144 max resid 0.1663818
Run 21 stress 5.669388e-05
... Procrustes: rmse 0.2658425 max resid 0.3644043
Run 22 stress 0
... Procrustes: rmse 0.06761828 max resid 0.1060347
Run 23 stress 8.752271e-05
... Procrustes: rmse 0.2270201 max resid 0.3203358
Run 24 stress 0
... Procrustes: rmse 0.02635104 max resid 0.03858882
Run 25 stress 9.720708e-05
... Procrustes: rmse 0.2171371 max resid 0.3050963
Run 26 stress 6.296699e-05
... Procrustes: rmse 0.2532506 max resid 0.3495244
Run 27 stress 0
... Procrustes: rmse 0.1516025 max resid 0.2168951
Run 28 stress 0
... Procrustes: rmse 0.2212991 max resid 0.3060809
Run 29 stress 0
... Procrustes: rmse 0.2117351 max resid 0.2917023
Run 30 stress 7.599806e-05
... Procrustes: rmse 0.2628509 max resid 0.3599699
Run 31 stress 1.231121e-05
... Procrustes: rmse 0.1028224 max resid 0.1519504
Run 32 stress 8.82991e-05
... Procrustes: rmse 0.0966383 max resid 0.1209726
Run 33 stress 0
... Procrustes: rmse 0.2169812 max resid 0.2993104
Run 34 stress 0
... Procrustes: rmse 0.08984891 max resid 0.1436508
Run 35 stress 9.537374e-05
... Procrustes: rmse 0.1208359 max resid 0.1603184
Run 36 stress 0
... Procrustes: rmse 0.23336 max resid 0.3234995
Run 37 stress 9.970693e-05
... Procrustes: rmse 0.2215751 max resid 0.3167446
Run 38 stress 9.653247e-05
... Procrustes: rmse 0.1720133 max resid 0.23857
Run 39 stress 0
... Procrustes: rmse 0.1055798 max resid 0.1563873
Run 40 stress 6.664626e-05
... Procrustes: rmse 0.2134482 max resid 0.3029879
Run 41 stress 9.112601e-05
... Procrustes: rmse 0.1787802 max resid 0.254843
Run 42 stress 0
... Procrustes: rmse 0.1204302 max resid 0.1471806
Run 43 stress 0
... Procrustes: rmse 0.101409 max resid 0.146566
Run 44 stress 0
... Procrustes: rmse 0.07364291 max resid 0.1044094
Run 45 stress 0
... Procrustes: rmse 0.1289976 max resid 0.1885612
Run 46 stress 8.911955e-05
... Procrustes: rmse 0.2472635 max resid 0.338419
Run 47 stress 9.568779e-05
... Procrustes: rmse 0.1485338 max resid 0.2108878
Run 48 stress 0
... Procrustes: rmse 0.1369993 max resid 0.1915395
Run 49 stress 9.450226e-05
... Procrustes: rmse 0.184388 max resid 0.2626905
Run 50 stress 0
... Procrustes: rmse 0.1418683 max resid 0.1911597
*** No convergence -- monoMDS stopping criteria:
50: stress < sminstress is (nearly) zero: you may have insufficient dataRun 0 stress 2.124151e-05
Run 1 stress 0
... New best solution
... Procrustes: rmse 0.157537 max resid 0.1820812
Run 2 stress 0.2672679
Run 3 stress 2.326337e-05
... Procrustes: rmse 0.1968781 max resid 0.2903448
Run 4 stress 0.2181459
Run 5 stress 0
... Procrustes: rmse 0.1989075 max resid 0.2604973
Run 6 stress 2.895186e-05
... Procrustes: rmse 0.1189694 max resid 0.1580015
Run 7 stress 0
... Procrustes: rmse 0.1004432 max resid 0.1273505
Run 8 stress 0.1557467
Run 9 stress 0
... Procrustes: rmse 0.06913063 max resid 0.1308691
Run 10 stress 0
... Procrustes: rmse 0.1227099 max resid 0.1738979
Run 11 stress 0
... Procrustes: rmse 0.1553157 max resid 0.2218817
Run 12 stress 0.1557467
Run 13 stress 0
... Procrustes: rmse 0.09654797 max resid 0.1534925
Run 14 stress 0
... Procrustes: rmse 0.06960064 max resid 0.1143846
Run 15 stress 0.1557467
Run 16 stress 0
... Procrustes: rmse 0.1095223 max resid 0.1417771
Run 17 stress 0
... Procrustes: rmse 0.1562131 max resid 0.1989529
Run 18 stress 0
... Procrustes: rmse 0.1710129 max resid 0.2870675
Run 19 stress 0
... Procrustes: rmse 0.07739966 max resid 0.1129684
Run 20 stress 0
... Procrustes: rmse 0.245458 max resid 0.317773
Run 21 stress 0
... Procrustes: rmse 0.1663953 max resid 0.2197683
Run 22 stress 0
... Procrustes: rmse 0.1121602 max resid 0.1511245
Run 23 stress 0
... Procrustes: rmse 0.1090136 max resid 0.1645137
Run 24 stress 9.515995e-05
... Procrustes: rmse 0.2035066 max resid 0.2618318
Run 25 stress 0
... Procrustes: rmse 0.06575861 max resid 0.08991442
Run 26 stress 0
... Procrustes: rmse 0.07866976 max resid 0.1228012
Run 27 stress 4.604018e-05
... Procrustes: rmse 0.08763106 max resid 0.1560758
Run 28 stress 0.2181459
Run 29 stress 0
... Procrustes: rmse 0.1129685 max resid 0.1687105
Run 30 stress 8.9267e-05
... Procrustes: rmse 0.1391779 max resid 0.2695348
Run 31 stress 0
... Procrustes: rmse 0.1337685 max resid 0.1803485
Run 32 stress 0
... Procrustes: rmse 0.1234044 max resid 0.1759336
Run 33 stress 9.15148e-05
... Procrustes: rmse 0.1537131 max resid 0.2566823
Run 34 stress 5.096189e-05
... Procrustes: rmse 0.1552274 max resid 0.1950005
Run 35 stress 0
... Procrustes: rmse 0.08441157 max resid 0.1170539
Run 36 stress 0.1557467
Run 37 stress 0
... Procrustes: rmse 0.1174666 max resid 0.167026
Run 38 stress 0
... Procrustes: rmse 0.1370138 max resid 0.1760105
Run 39 stress 0
... Procrustes: rmse 0.1448919 max resid 0.1990777
Run 40 stress 0.1557467
Run 41 stress 0
... Procrustes: rmse 0.1035365 max resid 0.1661832
Run 42 stress 0
... Procrustes: rmse 0.1670909 max resid 0.2460737
Run 43 stress 0
... Procrustes: rmse 0.1858107 max resid 0.2598215
Run 44 stress 0
... Procrustes: rmse 0.1935573 max resid 0.3249048
Run 45 stress 0
... Procrustes: rmse 0.1377805 max resid 0.2227059
Run 46 stress 0.1557467
Run 47 stress 0
... Procrustes: rmse 0.06953413 max resid 0.1013176
Run 48 stress 2.185571e-06
... Procrustes: rmse 0.1280379 max resid 0.1906299
Run 49 stress 0
... Procrustes: rmse 0.2123503 max resid 0.3384559
Run 50 stress 0
... Procrustes: rmse 0.1617054 max resid 0.2546631
*** No convergence -- monoMDS stopping criteria:
41: stress < smin
4: stress ratio > sratmax
5: scale factor of the gradient < sfgrmin
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 7.690157e-05
Run 1 stress 9.146088e-05
... Procrustes: rmse 0.1415001 max resid 0.1763404
Run 2 stress 0.2672326
Run 3 stress 5.403948e-05
... New best solution
... Procrustes: rmse 0.1011135 max resid 0.1415481
Run 4 stress 0.2181459
Run 5 stress 0
... New best solution
... Procrustes: rmse 0.07776228 max resid 0.1018469
Run 6 stress 9.884376e-05
... Procrustes: rmse 0.1517016 max resid 0.2350077
Run 7 stress 0
... Procrustes: rmse 0.1475991 max resid 0.2231857
Run 8 stress 0.2181459
Run 9 stress 9.661122e-05
... Procrustes: rmse 0.1461225 max resid 0.2112905
Run 10 stress 0
... Procrustes: rmse 0.1872038 max resid 0.357011
Run 11 stress 9.944485e-05
... Procrustes: rmse 0.1335075 max resid 0.1685597
Run 12 stress 0.2181459
Run 13 stress 0
... Procrustes: rmse 0.1499212 max resid 0.2717162
Run 14 stress 9.552391e-05
... Procrustes: rmse 0.1356566 max resid 0.1653038
Run 15 stress 0
... Procrustes: rmse 0.1651368 max resid 0.296857
Run 16 stress 9.842682e-05
... Procrustes: rmse 0.1155894 max resid 0.1949442
Run 17 stress 0
... Procrustes: rmse 0.1838935 max resid 0.3036845
Run 18 stress 0
... Procrustes: rmse 0.147089 max resid 0.2688188
Run 19 stress 9.382864e-05
... Procrustes: rmse 0.1224032 max resid 0.1626796
Run 20 stress 0
... Procrustes: rmse 0.152046 max resid 0.2392613
Run 21 stress 9.070282e-05
... Procrustes: rmse 0.1424178 max resid 0.2121383
Run 22 stress 9.709145e-05
... Procrustes: rmse 0.1396264 max resid 0.2046795
Run 23 stress 1.876887e-05
... Procrustes: rmse 0.06508739 max resid 0.09650626
Run 24 stress 9.572346e-05
... Procrustes: rmse 0.1028844 max resid 0.1323138
Run 25 stress 6.913225e-05
... Procrustes: rmse 0.1617473 max resid 0.2317607
Run 26 stress 0
... Procrustes: rmse 0.1152998 max resid 0.1599585
Run 27 stress 6.65981e-05
... Procrustes: rmse 0.08531527 max resid 0.114115
Run 28 stress 7.315397e-05
... Procrustes: rmse 0.1425555 max resid 0.2120054
Run 29 stress 9.803795e-05
... Procrustes: rmse 0.1453514 max resid 0.2374238
Run 30 stress 8.976881e-05
... Procrustes: rmse 0.07811154 max resid 0.09951727
Run 31 stress 0
... Procrustes: rmse 0.1090569 max resid 0.2016708
Run 32 stress 2.774429e-05
... Procrustes: rmse 0.03809068 max resid 0.06023713
Run 33 stress 0
... Procrustes: rmse 0.1406218 max resid 0.2167305
Run 34 stress 0
... Procrustes: rmse 0.1138822 max resid 0.2022683
Run 35 stress 0
... Procrustes: rmse 0.1485088 max resid 0.2909942
Run 36 stress 0
... Procrustes: rmse 0.2068267 max resid 0.3509717
Run 37 stress 9.81161e-05
... Procrustes: rmse 0.1136406 max resid 0.154546
Run 38 stress 8.929985e-05
... Procrustes: rmse 0.1602806 max resid 0.2425274
Run 39 stress 9.327513e-05
... Procrustes: rmse 0.06010741 max resid 0.09688332
Run 40 stress 0.2130992
Run 41 stress 0
... Procrustes: rmse 0.1728767 max resid 0.2812426
Run 42 stress 0.1557467
Run 43 stress 9.003367e-05
... Procrustes: rmse 0.09705937 max resid 0.1231281
Run 44 stress 0
... Procrustes: rmse 0.0784769 max resid 0.1002845
Run 45 stress 0
... Procrustes: rmse 0.1765949 max resid 0.3360861
Run 46 stress 0.1557467
Run 47 stress 8.141485e-05
... Procrustes: rmse 0.03671525 max resid 0.0492191
Run 48 stress 8.236852e-05
... Procrustes: rmse 0.1332011 max resid 0.2059865
Run 49 stress 0
... Procrustes: rmse 0.1164019 max resid 0.197308
Run 50 stress 9.072801e-05
... Procrustes: rmse 0.08213255 max resid 0.1202882
*** No convergence -- monoMDS stopping criteria:
43: stress < smin
2: stress ratio > sratmax
5: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 8.283428e-05
Run 1 stress 5.053448e-05
... New best solution
... Procrustes: rmse 0.1922255 max resid 0.2868384
Run 2 stress 4.103509e-05
... New best solution
... Procrustes: rmse 0.08844274 max resid 0.1416263
Run 3 stress 6.233269e-05
... Procrustes: rmse 0.2923098 max resid 0.5834878
Run 4 stress 8.698547e-05
... Procrustes: rmse 0.07897695 max resid 0.1104613
Run 5 stress 0.0002641342
... Procrustes: rmse 0.1499903 max resid 0.2000758
Run 6 stress 9.342291e-05
... Procrustes: rmse 0.1526383 max resid 0.2268521
Run 7 stress 6.822434e-05
... Procrustes: rmse 0.00930892 max resid 0.01528143
Run 8 stress 8.709558e-05
... Procrustes: rmse 0.1643062 max resid 0.2493343
Run 9 stress 9.432742e-05
... Procrustes: rmse 0.2039125 max resid 0.3340389
Run 10 stress 5.844301e-05
... Procrustes: rmse 0.1747382 max resid 0.3067221
Run 11 stress 9.479429e-05
... Procrustes: rmse 0.2304286 max resid 0.3665073
Run 12 stress 1.000118e-05
... New best solution
... Procrustes: rmse 0.1422626 max resid 0.2404533
Run 13 stress 9.185907e-05
... Procrustes: rmse 0.06704519 max resid 0.107192
Run 14 stress 8.85248e-05
... Procrustes: rmse 0.07529254 max resid 0.09924686
Run 15 stress 9.914424e-05
... Procrustes: rmse 0.05604204 max resid 0.0821208
Run 16 stress 9.616633e-05
... Procrustes: rmse 0.2512455 max resid 0.4208675
Run 17 stress 9.977291e-05
... Procrustes: rmse 0.23865 max resid 0.4255241
Run 18 stress 8.79261e-05
... Procrustes: rmse 0.08351139 max resid 0.1501583
Run 19 stress 8.982621e-05
... Procrustes: rmse 0.07555638 max resid 0.1094195
Run 20 stress 0
... New best solution
... Procrustes: rmse 0.1506845 max resid 0.2355684
Run 21 stress 1.97294e-05
... Procrustes: rmse 0.0875201 max resid 0.1458886
Run 22 stress 9.106491e-05
... Procrustes: rmse 0.1151792 max resid 0.1482155
Run 23 stress 7.758199e-05
... Procrustes: rmse 0.1405023 max resid 0.1869294
Run 24 stress 0
... Procrustes: rmse 0.1561257 max resid 0.2380979
Run 25 stress 0
... Procrustes: rmse 0.1113668 max resid 0.1961744
Run 26 stress 0
... Procrustes: rmse 0.1376971 max resid 0.1880477
Run 27 stress 9.97782e-05
... Procrustes: rmse 0.1220643 max resid 0.169902
Run 28 stress 9.795589e-05
... Procrustes: rmse 0.1778349 max resid 0.3706085
Run 29 stress 0
... Procrustes: rmse 0.1210496 max resid 0.1725115
Run 30 stress 9.244629e-05
... Procrustes: rmse 0.1236686 max resid 0.1605261
Run 31 stress 8.785011e-05
... Procrustes: rmse 0.2074715 max resid 0.3033475
Run 32 stress 9.960447e-05
... Procrustes: rmse 0.1992931 max resid 0.2934866
Run 33 stress 9.658745e-05
... Procrustes: rmse 0.1970808 max resid 0.3035161
Run 34 stress 2.344357e-05
... Procrustes: rmse 0.1839445 max resid 0.2925193
Run 35 stress 0
... Procrustes: rmse 0.1450726 max resid 0.1993992
Run 36 stress 7.866307e-05
... Procrustes: rmse 0.1696241 max resid 0.2543011
Run 37 stress 1.002386e-05
... Procrustes: rmse 0.2107818 max resid 0.3641185
Run 38 stress 0
... Procrustes: rmse 0.1925185 max resid 0.3438176
Run 39 stress 0.0003674189
... Procrustes: rmse 0.1415747 max resid 0.2053768
Run 40 stress 4.748581e-05
... Procrustes: rmse 0.2080998 max resid 0.3595177
Run 41 stress 9.761551e-05
... Procrustes: rmse 0.1485312 max resid 0.2058994
Run 42 stress 9.653015e-05
... Procrustes: rmse 0.2155408 max resid 0.3141755
Run 43 stress 9.827622e-05
... Procrustes: rmse 0.1938504 max resid 0.3872057
Run 44 stress 9.879833e-05
... Procrustes: rmse 0.1387625 max resid 0.1952533
Run 45 stress 9.581024e-05
... Procrustes: rmse 0.2254252 max resid 0.3182791
Run 46 stress 0
... Procrustes: rmse 0.166148 max resid 0.2369382
Run 47 stress 0
... Procrustes: rmse 0.1029269 max resid 0.1752032
Run 48 stress 5.557643e-05
... Procrustes: rmse 0.1230135 max resid 0.1761402
Run 49 stress 9.470078e-05
... Procrustes: rmse 0.1124555 max resid 0.1502745
Run 50 stress 9.891358e-05
... Procrustes: rmse 0.1378317 max resid 0.1765846
*** No convergence -- monoMDS stopping criteria:
2: no. of iterations >= maxit
48: stress < sminstress is (nearly) zero: you may have insufficient data
[[1]]
[[1]][[1]]
[[1]][[1]]$x
[1] 0.01726958 0.15581134 0.13147161 0.13253152 0.13572321 0.15194484 0.13535630 0.13626957
[9] 0.13917859 0.22884030 0.21698238 0.22878422 0.02160788 0.02158823 0.02048543
[[1]][[1]]$y
[1] 0.01020047 0.29896654 0.24744217 0.25458135 0.26089179 0.29132468 0.25614336 0.26246972
[9] 0.26897552 0.44058375 0.42222387 0.43240206 0.03572964 0.03055534 0.01029507
[[1]][[1]]$yf
[1] 0.01020047 0.29896654 0.24744217 0.25458135 0.26089179 0.29132468 0.25614336 0.26246972
[9] 0.26897552 0.44058375 0.42222387 0.43240206 0.03572964 0.03055534 0.01029507
[[1]][[2]]
[[2]]
[[2]][[1]]
[[2]][[1]]$x
[1] 0.01147500 0.02600497 0.13253819 0.13465471 0.13565457 0.02260174 0.13312275 0.13511137
[9] 0.13596663 0.14185829 0.14404187 0.14516469 0.01854379 0.02257133 0.02236667
[[2]][[1]]$y
[1] 0.02187808 0.13623262 0.16095319 0.21033630 0.23620898 0.12830338 0.17555454 0.22308581
[9] 0.25492757 0.29516355 0.34583188 0.35917942 0.05274279 0.09783240 0.09703298
[[2]][[1]]$yf
[1] 0.02187808 0.13623262 0.16095319 0.21033630 0.23620898 0.12830338 0.17555454 0.22308581
[9] 0.25492757 0.29516355 0.34583188 0.35917942 0.05274279 0.09783240 0.09703298
[[2]][[2]]
[[3]]
[[3]][[1]]
[[3]][[1]]$x
[1] 0.01407066 0.03006718 0.17570586 0.17886640 0.18439056 0.02975746 0.17130494 0.17531223
[9] 0.17960375 0.18602513 0.18952329 0.19520634 0.01703427 0.02094083 0.01595125
[[3]][[1]]$y
[1] 0.05546866 0.20631279 0.30115446 0.34739099 0.42809877 0.20318227 0.25843910 0.29706766
[9] 0.37673420 0.43138862 0.43908097 0.50572827 0.08787990 0.15932259 0.08198425
[[3]][[1]]$yf
[1] 0.05546866 0.20631279 0.30115446 0.34739099 0.42809877 0.20318227 0.25843910 0.29706766
[9] 0.37673420 0.43138862 0.43908097 0.50572827 0.08787990 0.15932259 0.08198425
[[3]][[2]]
[[4]]
[[4]][[1]]
[[4]][[1]]$x
[1] 0.56381918 0.61080106 0.61709803 0.75387552 0.47348778 0.07365421 0.07352797 0.26547630
[9] 0.15229718 0.01959458 0.27039870 0.20200411 0.26224033 0.20489328 0.31007686
[[4]][[1]]$y
[1] 1.01461542 1.26889112 1.27517861 1.53062084 0.91038711 0.33630473 0.31910553 0.55271222
[9] 0.34940444 0.04138279 0.55546217 0.39501444 0.51471766 0.41640377 0.85816245
[[4]][[1]]$yf
[1] 1.01461542 1.26889112 1.27517861 1.53062084 0.91038711 0.33630473 0.31910553 0.55271222
[9] 0.34940444 0.04138279 0.55546217 0.39501444 0.51471766 0.41640377 0.85816245
[[4]][[2]]
# analyze sample F1_25
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F1_25")'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 9.687844e-05
Run 1 stress 0.1557467
Run 2 stress 9.923749e-05
... Procrustes: rmse 0.1533171 max resid 0.2285749
Run 3 stress 0.0001119639
... Procrustes: rmse 0.08191147 max resid 0.148517
Run 4 stress 8.864365e-05
... New best solution
... Procrustes: rmse 0.144963 max resid 0.2148241
Run 5 stress 0.2672977
Run 6 stress 0.2269841
Run 7 stress 8.999738e-05
... Procrustes: rmse 0.1467988 max resid 0.2523924
Run 8 stress 0.2280378
Run 9 stress 0.2269843
Run 10 stress 8.655578e-05
... New best solution
... Procrustes: rmse 0.1125068 max resid 0.1987212
Run 11 stress 0.2181459
Run 12 stress 9.788223e-05
... Procrustes: rmse 0.0742367 max resid 0.1426694
Run 13 stress 9.570262e-05
... Procrustes: rmse 0.1070376 max resid 0.1794512
Run 14 stress 0.0001993861
... Procrustes: rmse 0.1517728 max resid 0.2082663
Run 15 stress 0.2280378
Run 16 stress 9.743709e-05
... Procrustes: rmse 0.09235497 max resid 0.14615
Run 17 stress 7.999889e-05
... New best solution
... Procrustes: rmse 0.1573976 max resid 0.2225491
Run 18 stress 3.467416e-06
... New best solution
... Procrustes: rmse 0.1317174 max resid 0.2237248
Run 19 stress 7.523051e-05
... Procrustes: rmse 0.1777496 max resid 0.3632702
Run 20 stress 9.124949e-05
... Procrustes: rmse 0.04711426 max resid 0.06562554
Run 21 stress 9.751295e-05
... Procrustes: rmse 0.1184699 max resid 0.1904849
Run 22 stress 0.001139641
Run 23 stress 9.789778e-05
... Procrustes: rmse 0.06606032 max resid 0.1170474
Run 24 stress 6.164918e-05
... Procrustes: rmse 0.1904947 max resid 0.3499253
Run 25 stress 0.2673072
Run 26 stress 5.766115e-05
... Procrustes: rmse 0.03537994 max resid 0.05763187
Run 27 stress 9.787711e-05
... Procrustes: rmse 0.1334064 max resid 0.2417075
Run 28 stress 0.1557467
Run 29 stress 0.1557467
Run 30 stress 0.2673848
Run 31 stress 9.956824e-05
... Procrustes: rmse 0.1227267 max resid 0.1897208
Run 32 stress 9.078131e-05
... Procrustes: rmse 0.1271666 max resid 0.2293123
Run 33 stress 5.211457e-05
... Procrustes: rmse 0.1032831 max resid 0.1645338
Run 34 stress 7.982201e-05
... Procrustes: rmse 0.04556295 max resid 0.06692898
Run 35 stress 9.515436e-05
... Procrustes: rmse 0.09012572 max resid 0.1428019
Run 36 stress 0.2269844
Run 37 stress 9.464142e-05
... Procrustes: rmse 0.1650476 max resid 0.2698445
Run 38 stress 0.2672679
Run 39 stress 9.806596e-05
... Procrustes: rmse 0.133992 max resid 0.2726458
Run 40 stress 9.567766e-05
... Procrustes: rmse 0.1144961 max resid 0.1794957
Run 41 stress 0.0001243479
... Procrustes: rmse 0.1413484 max resid 0.2321469
Run 42 stress 9.129546e-05
... Procrustes: rmse 0.1007795 max resid 0.1415661
Run 43 stress 8.906618e-05
... Procrustes: rmse 0.197313 max resid 0.3598438
Run 44 stress 0.0003167431
... Procrustes: rmse 0.1753701 max resid 0.3378403
Run 45 stress 8.68979e-05
... Procrustes: rmse 0.06420183 max resid 0.09655708
Run 46 stress 9.695053e-05
... Procrustes: rmse 0.09478322 max resid 0.1725051
Run 47 stress 9.455382e-05
... Procrustes: rmse 0.1617895 max resid 0.2704003
Run 48 stress 0.2269842
Run 49 stress 0.2269841
Run 50 stress 8.596888e-05
... Procrustes: rmse 0.184057 max resid 0.3786299
*** No convergence -- monoMDS stopping criteria:
5: no. of iterations >= maxit
30: stress < smin
8: stress ratio > sratmax
7: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient dataRun 0 stress 0
Run 1 stress 0.1970951
Run 2 stress 8.863024e-05
... Procrustes: rmse 0.1435982 max resid 0.2352675
Run 3 stress 0
... Procrustes: rmse 0.07573411 max resid 0.1322859
Run 4 stress 0.1967694
Run 5 stress 0
... Procrustes: rmse 0.1745504 max resid 0.2421015
Run 6 stress 0.2673072
Run 7 stress 0
... Procrustes: rmse 0.09187165 max resid 0.1294829
Run 8 stress 0.1557467
Run 9 stress 0.268042
Run 10 stress 0.2680416
Run 11 stress 0
... Procrustes: rmse 0.1384206 max resid 0.2284591
Run 12 stress 0.1557467
Run 13 stress 0
... Procrustes: rmse 0.1427498 max resid 0.1988049
Run 14 stress 0.001800982
Run 15 stress 9.393584e-05
... Procrustes: rmse 0.1385404 max resid 0.2010457
Run 16 stress 0
... Procrustes: rmse 0.1423701 max resid 0.2850375
Run 17 stress 0
... Procrustes: rmse 0.141683 max resid 0.230586
Run 18 stress 0
... Procrustes: rmse 0.1411673 max resid 0.2245337
Run 19 stress 7.634129e-05
... Procrustes: rmse 0.0351002 max resid 0.06229319
Run 20 stress 8.105708e-05
... Procrustes: rmse 0.1945288 max resid 0.2835619
Run 21 stress 0
... Procrustes: rmse 0.168572 max resid 0.2385113
Run 22 stress 0.2181459
Run 23 stress 3.190408e-05
... Procrustes: rmse 0.1464289 max resid 0.2353091
Run 24 stress 2.075549e-05
... Procrustes: rmse 0.1576329 max resid 0.217991
Run 25 stress 9.794157e-05
... Procrustes: rmse 0.1410846 max resid 0.2314156
Run 26 stress 0
... Procrustes: rmse 0.1513367 max resid 0.2221781
Run 27 stress 0.001538192
Run 28 stress 0.1967694
Run 29 stress 0.2181459
Run 30 stress 9.716556e-05
... Procrustes: rmse 0.1598371 max resid 0.2146547
Run 31 stress 0
... Procrustes: rmse 0.08348614 max resid 0.1269381
Run 32 stress 0
... Procrustes: rmse 0.1540523 max resid 0.2627928
Run 33 stress 0.2680415
Run 34 stress 0
... Procrustes: rmse 0.09358032 max resid 0.1352798
Run 35 stress 0
... Procrustes: rmse 0.1373076 max resid 0.2203163
Run 36 stress 0.1557467
Run 37 stress 8.116013e-06
... Procrustes: rmse 0.1423874 max resid 0.2266262
Run 38 stress 0
... Procrustes: rmse 0.139295 max resid 0.2282558
Run 39 stress 0
... Procrustes: rmse 0.08156026 max resid 0.1277834
Run 40 stress 9.540915e-05
... Procrustes: rmse 0.1434018 max resid 0.2426756
Run 41 stress 0
... Procrustes: rmse 0.16078 max resid 0.2929022
Run 42 stress 0.2181459
Run 43 stress 8.491538e-05
... Procrustes: rmse 0.1351561 max resid 0.2129919
Run 44 stress 0
... Procrustes: rmse 0.1783136 max resid 0.2768127
Run 45 stress 0.2673072
Run 46 stress 0.1557467
Run 47 stress 9.26848e-05
... Procrustes: rmse 0.1518318 max resid 0.2034258
Run 48 stress 0.2181459
Run 49 stress 9.688086e-05
... Procrustes: rmse 0.1772886 max resid 0.3425121
Run 50 stress 0
... Procrustes: rmse 0.09019609 max resid 0.142133
*** No convergence -- monoMDS stopping criteria:
2: no. of iterations >= maxit
32: stress < smin
7: stress ratio > sratmax
9: scale factor of the gradient < sfgrmin
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0
Run 1 stress 0
... Procrustes: rmse 0.1705458 max resid 0.258408
Run 2 stress 0.2672326
Run 3 stress 0
... Procrustes: rmse 0.157289 max resid 0.2113419
Run 4 stress 9.415192e-05
... Procrustes: rmse 0.16422 max resid 0.1964474
Run 5 stress 0.1557467
Run 6 stress 0.2269841
Run 7 stress 0
... Procrustes: rmse 0.1370493 max resid 0.1880191
Run 8 stress 0.2130991
Run 9 stress 5.3319e-05
... Procrustes: rmse 0.1805194 max resid 0.2833489
Run 10 stress 0
... Procrustes: rmse 0.1778136 max resid 0.2268477
Run 11 stress 0
... Procrustes: rmse 0.1861077 max resid 0.2705517
Run 12 stress 0.1557467
Run 13 stress 0
... Procrustes: rmse 0.144196 max resid 0.2196323
Run 14 stress 0
... Procrustes: rmse 0.1579901 max resid 0.2615369
Run 15 stress 0.2181459
Run 16 stress 0
... Procrustes: rmse 0.1756941 max resid 0.309202
Run 17 stress 0
... Procrustes: rmse 0.16738 max resid 0.2239437
Run 18 stress 0
... Procrustes: rmse 0.1699554 max resid 0.2485559
Run 19 stress 0
... Procrustes: rmse 0.1272678 max resid 0.2034016
Run 20 stress 0
... Procrustes: rmse 0.168326 max resid 0.2256897
Run 21 stress 0
... Procrustes: rmse 0.1352342 max resid 0.1782558
Run 22 stress 0
... Procrustes: rmse 0.1764752 max resid 0.2271915
Run 23 stress 0
... Procrustes: rmse 0.1481683 max resid 0.2013022
Run 24 stress 0
... Procrustes: rmse 0.1304938 max resid 0.1814899
Run 25 stress 0
... Procrustes: rmse 0.1233189 max resid 0.1655978
Run 26 stress 0
... Procrustes: rmse 0.2059416 max resid 0.3113155
Run 27 stress 0
... Procrustes: rmse 0.2004589 max resid 0.3548216
Run 28 stress 0.2761338
Run 29 stress 0
... Procrustes: rmse 0.1397922 max resid 0.2175155
Run 30 stress 0.1557467
Run 31 stress 0
... Procrustes: rmse 0.0416891 max resid 0.07656757
Run 32 stress 8.177802e-05
... Procrustes: rmse 0.176452 max resid 0.2834923
Run 33 stress 0.2761339
Run 34 stress 0
... Procrustes: rmse 0.1464626 max resid 0.1924123
Run 35 stress 0
... Procrustes: rmse 0.05062323 max resid 0.09048377
Run 36 stress 0.1557467
Run 37 stress 0
... Procrustes: rmse 0.1389981 max resid 0.1966292
Run 38 stress 0.2269844
Run 39 stress 0
... Procrustes: rmse 0.1639106 max resid 0.2207765
Run 40 stress 0.213099
Run 41 stress 0
... Procrustes: rmse 0.1357025 max resid 0.2083207
Run 42 stress 3.013413e-06
... Procrustes: rmse 0.187113 max resid 0.2192378
Run 43 stress 0
... Procrustes: rmse 0.1254231 max resid 0.1715368
Run 44 stress 0
... Procrustes: rmse 0.1126143 max resid 0.1384301
Run 45 stress 9.953427e-05
... Procrustes: rmse 0.1953697 max resid 0.2476744
Run 46 stress 0
... Procrustes: rmse 0.1762942 max resid 0.2161173
Run 47 stress 0
... Procrustes: rmse 0.1279137 max resid 0.1761318
Run 48 stress 0.2269841
Run 49 stress 0.1967694
Run 50 stress 9.617081e-05
... Procrustes: rmse 0.14562 max resid 0.1815759
*** No convergence -- monoMDS stopping criteria:
36: stress < smin
8: stress ratio > sratmax
6: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 3.789846e-05
Run 1 stress 0
... New best solution
... Procrustes: rmse 0.1145109 max resid 0.1536262
Run 2 stress 0
... Procrustes: rmse 0.07980724 max resid 0.1213707
Run 3 stress 0.2280383
Run 4 stress 0.2280379
Run 5 stress 6.542467e-05
... Procrustes: rmse 0.1073094 max resid 0.1471331
Run 6 stress 5.310242e-05
... Procrustes: rmse 0.099313 max resid 0.1480259
Run 7 stress 0.2269841
Run 8 stress 0
... Procrustes: rmse 0.1251073 max resid 0.1871881
Run 9 stress 0.1470313
Run 10 stress 2.422631e-05
... Procrustes: rmse 0.1746746 max resid 0.2877084
Run 11 stress 9.933525e-05
... Procrustes: rmse 0.1284031 max resid 0.1674343
Run 12 stress 7.507572e-05
... Procrustes: rmse 0.1084557 max resid 0.1772481
Run 13 stress 8.795388e-05
... Procrustes: rmse 0.1648853 max resid 0.3352339
Run 14 stress 9.305106e-05
... Procrustes: rmse 0.1364532 max resid 0.2236201
Run 15 stress 9.089248e-05
... Procrustes: rmse 0.1557531 max resid 0.2275573
Run 16 stress 3.884471e-05
... Procrustes: rmse 0.1223032 max resid 0.1587061
Run 17 stress 0
... Procrustes: rmse 0.2005014 max resid 0.2769815
Run 18 stress 0
... Procrustes: rmse 0.1390127 max resid 0.1903621
Run 19 stress 0.2269848
Run 20 stress 0
... Procrustes: rmse 0.1159614 max resid 0.1483723
Run 21 stress 0
... Procrustes: rmse 0.1153129 max resid 0.2018715
Run 22 stress 0.0002311025
... Procrustes: rmse 0.1195525 max resid 0.192406
Run 23 stress 0.2269842
Run 24 stress 0
... Procrustes: rmse 0.1199807 max resid 0.230077
Run 25 stress 0
... Procrustes: rmse 0.08480633 max resid 0.1396733
Run 26 stress 0.2672326
Run 27 stress 9.693395e-05
... Procrustes: rmse 0.1477368 max resid 0.1747972
Run 28 stress 0
... Procrustes: rmse 0.1298133 max resid 0.1910607
Run 29 stress 9.842799e-05
... Procrustes: rmse 0.1501976 max resid 0.240227
Run 30 stress 6.974657e-05
... Procrustes: rmse 0.1566156 max resid 0.2573867
Run 31 stress 9.598797e-05
... Procrustes: rmse 0.1379217 max resid 0.2111452
Run 32 stress 0.1470313
Run 33 stress 0.1470313
Run 34 stress 0
... Procrustes: rmse 0.156685 max resid 0.2533842
Run 35 stress 6.562241e-05
... Procrustes: rmse 0.1469278 max resid 0.2496937
Run 36 stress 7.219999e-05
... Procrustes: rmse 0.1355018 max resid 0.207459
Run 37 stress 3.258315e-05
... Procrustes: rmse 0.1384994 max resid 0.2127513
Run 38 stress 1.278042e-05
... Procrustes: rmse 0.1218566 max resid 0.1639386
Run 39 stress 0.2280378
Run 40 stress 8.471215e-05
... Procrustes: rmse 0.1198378 max resid 0.1903936
Run 41 stress 0.2761346
Run 42 stress 0
... Procrustes: rmse 0.09595647 max resid 0.1242599
Run 43 stress 0
... Procrustes: rmse 0.1387718 max resid 0.2385281
Run 44 stress 0
... Procrustes: rmse 0.1483169 max resid 0.2262966
Run 45 stress 3.098039e-05
... Procrustes: rmse 0.1092554 max resid 0.1948524
Run 46 stress 0
... Procrustes: rmse 0.06070262 max resid 0.08421746
Run 47 stress 0.2181459
Run 48 stress 7.805801e-05
... Procrustes: rmse 0.07728058 max resid 0.1204784
Run 49 stress 0
... Procrustes: rmse 0.0237783 max resid 0.03514742
Run 50 stress 3.37506e-05
... Procrustes: rmse 0.1498576 max resid 0.2484866
*** No convergence -- monoMDS stopping criteria:
1: no. of iterations >= maxit
37: stress < smin
11: stress ratio > sratmax
1: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
[[1]]
[[1]][[1]]
[[1]][[1]]$x
[1] 0.010276771 0.028006530 0.107695950 0.094996348 0.107372465 0.021527691 0.100707321
[8] 0.090464075 0.101828851 0.092082292 0.081859348 0.093356332 0.015829221 0.008802222
[15] 0.016902250
[[1]][[1]]$y
[1] 3.561561e-02 1.400662e-01 2.416203e-01 1.580440e-01 2.416230e-01 1.073651e-01
[7] 2.251203e-01 1.536915e-01 2.251240e-01 1.578027e-01 1.414274e-01 1.578095e-01
[13] 1.034024e-01 8.594704e-06 1.034010e-01
[[1]][[1]]$yf
[1] 3.561561e-02 1.400662e-01 2.416217e-01 1.580440e-01 2.416217e-01 1.073651e-01
[7] 2.251203e-01 1.536915e-01 2.251240e-01 1.578027e-01 1.414274e-01 1.578095e-01
[13] 1.034017e-01 8.594704e-06 1.034017e-01
[[1]][[2]]
[[2]]
[[2]][[1]]
[[2]][[1]]$x
[1] 0.009204274 0.012510909 0.145301457 0.140910131 0.131301795 0.013939975 0.148432054
[8] 0.146012469 0.135231175 0.151513032 0.147485051 0.136565349 0.012108382 0.028509969
[15] 0.019328989
[[2]][[1]]$y
[1] 0.01297275 0.02151786 0.28494911 0.27773623 0.25740454 0.02455355 0.29474942 0.28807366
[9] 0.26850482 0.30047462 0.29227485 0.27027084 0.02005695 0.05415278 0.03483280
[[2]][[1]]$yf
[1] 0.01297275 0.02151786 0.28494911 0.27773623 0.25740454 0.02455355 0.29474942 0.28807366
[9] 0.26850482 0.30047462 0.29227485 0.27027084 0.02005695 0.05415278 0.03483280
[[2]][[2]]
[[3]]
[[3]][[1]]
[[3]][[1]]$x
[1] 0.014031405 0.016757193 0.157854448 0.152590813 0.174071046 0.011473208 0.166926445
[8] 0.161662441 0.182798589 0.168654974 0.163319190 0.188210584 0.009348346 0.023415623
[15] 0.029340380
[[3]][[1]]$y
[1] 0.03165700 0.04362705 0.30927524 0.29868014 0.34304689 0.02477087 0.34055452 0.32990048
[9] 0.37469035 0.34236923 0.33147682 0.37933891 0.01125719 0.05458582 0.06401029
[[3]][[1]]$yf
[1] 0.03165700 0.04362705 0.30927524 0.29868014 0.34304689 0.02477087 0.34055452 0.32990048
[9] 0.37469035 0.34236923 0.33147682 0.37933891 0.01125719 0.05458582 0.06401029
[[3]][[2]]
[[4]]
[[4]][[1]]
[[4]][[1]]$x
[1] 0.01914216 0.21726900 0.23455085 0.21518440 0.01491085 0.20553890 0.22343359 0.20400739
[9] 0.01923528 0.05234390 0.01582745 0.21060733 0.04196642 0.22578779 0.20782168
[[4]][[1]]$y
[1] 0.09545722 0.44584428 0.57292705 0.43852141 0.05098696 0.39406134 0.48595284 0.37572478
[9] 0.09908254 0.28732052 0.06096461 0.40106049 0.23160967 0.54643323 0.39771852
[[4]][[1]]$yf
[1] 0.09545722 0.44584428 0.57292705 0.43852141 0.05098696 0.39406134 0.48595284 0.37572478
[9] 0.09908254 0.28732052 0.06096461 0.40106049 0.23160967 0.54643323 0.39771852
[[4]][[2]]
Filler 2
# analyze sample F2_25
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F2_25")'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0
Run 1 stress 0
... Procrustes: rmse 0.1223407 max resid 0.2065314
Run 2 stress 0.2269841
Run 3 stress 3.808975e-05
... Procrustes: rmse 0.03470198 max resid 0.05781481
Run 4 stress 3.962713e-05
... Procrustes: rmse 0.1259084 max resid 0.1986445
Run 5 stress 0.2673072
Run 6 stress 0.2673072
Run 7 stress 2.205653e-05
... Procrustes: rmse 0.04972731 max resid 0.08872897
Run 8 stress 0.2181459
Run 9 stress 0.2280378
Run 10 stress 0.2280378
Run 11 stress 0
... Procrustes: rmse 0.06448306 max resid 0.1136125
Run 12 stress 0.2181459
Run 13 stress 9.985475e-05
... Procrustes: rmse 0.0517174 max resid 0.08527064
Run 14 stress 9.899937e-05
... Procrustes: rmse 0.1113123 max resid 0.2122332
Run 15 stress 0.2181459
Run 16 stress 9.795406e-05
... Procrustes: rmse 0.08753915 max resid 0.1569129
Run 17 stress 6.791436e-05
... Procrustes: rmse 0.08009044 max resid 0.1546942
Run 18 stress 9.01363e-05
... Procrustes: rmse 0.1734251 max resid 0.2695782
Run 19 stress 9.70686e-05
... Procrustes: rmse 0.06170932 max resid 0.09265697
Run 20 stress 0
... Procrustes: rmse 0.1063635 max resid 0.1803731
Run 21 stress 0.2761345
Run 22 stress 0.2280378
Run 23 stress 0.2269842
Run 24 stress 1.753162e-06
... Procrustes: rmse 0.1362969 max resid 0.2664801
Run 25 stress 9.648186e-05
... Procrustes: rmse 0.17437 max resid 0.2406629
Run 26 stress 0.2269842
Run 27 stress 0
... Procrustes: rmse 0.07956493 max resid 0.1535682
Run 28 stress 0.1557467
Run 29 stress 0.2181459
Run 30 stress 0.2761351
Run 31 stress 0
... Procrustes: rmse 0.09839422 max resid 0.1634292
Run 32 stress 1.586183e-05
... Procrustes: rmse 0.09377439 max resid 0.1662404
Run 33 stress 0.2280378
Run 34 stress 0
... Procrustes: rmse 0.09625735 max resid 0.1736505
Run 35 stress 0
... Procrustes: rmse 0.115005 max resid 0.2255002
Run 36 stress 0.2181459
Run 37 stress 1.222857e-05
... Procrustes: rmse 0.1341828 max resid 0.2640306
Run 38 stress 0.2181459
Run 39 stress 0
... Procrustes: rmse 0.1255493 max resid 0.2193809
Run 40 stress 0.1557467
Run 41 stress 4.061211e-05
... Procrustes: rmse 0.1479707 max resid 0.2413699
Run 42 stress 0.1557467
Run 43 stress 0
... Procrustes: rmse 0.1343411 max resid 0.1873171
Run 44 stress 0
... Procrustes: rmse 0.1136744 max resid 0.1608809
Run 45 stress 9.407156e-05
... Procrustes: rmse 0.1388031 max resid 0.2574358
Run 46 stress 0.1557467
Run 47 stress 9.050593e-05
... Procrustes: rmse 0.08127762 max resid 0.1039046
Run 48 stress 0.2181459
Run 49 stress 2.867819e-06
... Procrustes: rmse 0.08714716 max resid 0.1390009
Run 50 stress 0
... Procrustes: rmse 0.1826178 max resid 0.2214629
*** No convergence -- monoMDS stopping criteria:
28: stress < smin
11: stress ratio > sratmax
11: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient dataRun 0 stress 9.985172e-05
Run 1 stress 9.675443e-05
... New best solution
... Procrustes: rmse 0.1121075 max resid 0.16909
Run 2 stress 0.2672326
Run 3 stress 5.260601e-05
... New best solution
... Procrustes: rmse 0.1873748 max resid 0.3449788
Run 4 stress 0.2181459
Run 5 stress 8.672378e-05
... Procrustes: rmse 0.1560671 max resid 0.3048849
Run 6 stress 0.0005296485
... Procrustes: rmse 0.1125914 max resid 0.1893854
Run 7 stress 9.652097e-05
... Procrustes: rmse 0.07691796 max resid 0.1103629
Run 8 stress 0.1967694
Run 9 stress 9.317782e-05
... Procrustes: rmse 0.1476974 max resid 0.239694
Run 10 stress 0.000380964
... Procrustes: rmse 0.1222537 max resid 0.2026794
Run 11 stress 9.856839e-05
... Procrustes: rmse 0.1873439 max resid 0.3614229
Run 12 stress 0.1967694
Run 13 stress 8.78091e-05
... Procrustes: rmse 0.1146169 max resid 0.1894954
Run 14 stress 9.772237e-05
... Procrustes: rmse 0.1873746 max resid 0.3614209
Run 15 stress 0.1967694
Run 16 stress 8.102647e-05
... Procrustes: rmse 0.02544929 max resid 0.04248186
Run 17 stress 0.0002723167
... Procrustes: rmse 0.07291899 max resid 0.1215051
Run 18 stress 6.687593e-05
... Procrustes: rmse 0.1024989 max resid 0.1828397
Run 19 stress 0.0003314391
... Procrustes: rmse 0.06026818 max resid 0.08163546
Run 20 stress 0.0008100836
Run 21 stress 0.2761354
Run 22 stress 9.327674e-05
... Procrustes: rmse 0.1467346 max resid 0.2297215
Run 23 stress 9.58317e-05
... Procrustes: rmse 0.1873755 max resid 0.3614206
Run 24 stress 9.644516e-05
... Procrustes: rmse 0.1873504 max resid 0.3614331
Run 25 stress 5.47417e-05
... Procrustes: rmse 0.1528847 max resid 0.2991829
Run 26 stress 9.823018e-05
... Procrustes: rmse 0.187374 max resid 0.3614191
Run 27 stress 9.355997e-05
... Procrustes: rmse 0.1060145 max resid 0.135654
Run 28 stress 0.1557467
Run 29 stress 9.962881e-05
... Procrustes: rmse 0.1742841 max resid 0.2752743
Run 30 stress 0.2761355
Run 31 stress 8.863165e-05
... Procrustes: rmse 0.1000191 max resid 0.1429708
Run 32 stress 0
... New best solution
... Procrustes: rmse 0.03823748 max resid 0.04827639
Run 33 stress 9.071665e-05
... Procrustes: rmse 0.143425 max resid 0.2246148
Run 34 stress 9.690772e-05
... Procrustes: rmse 0.1444007 max resid 0.2397992
Run 35 stress 0.0004818294
... Procrustes: rmse 0.1049189 max resid 0.1677734
Run 36 stress 0.1967694
Run 37 stress 0
... Procrustes: rmse 0.07100411 max resid 0.1226793
Run 38 stress 8.649133e-05
... Procrustes: rmse 0.1750531 max resid 0.2757394
Run 39 stress 7.138159e-05
... Procrustes: rmse 0.05292396 max resid 0.09925639
Run 40 stress 0.1557467
Run 41 stress 9.669527e-05
... Procrustes: rmse 0.2071734 max resid 0.3785128
Run 42 stress 0.1557467
Run 43 stress 9.815866e-05
... Procrustes: rmse 0.08782676 max resid 0.1677413
Run 44 stress 9.674277e-05
... Procrustes: rmse 0.2071389 max resid 0.3783266
Run 45 stress 8.906234e-05
... Procrustes: rmse 0.1122843 max resid 0.182787
Run 46 stress 0.1557467
Run 47 stress 8.844247e-05
... Procrustes: rmse 0.06604213 max resid 0.09233335
Run 48 stress 8.753266e-05
... Procrustes: rmse 0.1351628 max resid 0.2157915
Run 49 stress 9.186879e-05
... Procrustes: rmse 0.2071415 max resid 0.3783281
Run 50 stress 8.507341e-05
... Procrustes: rmse 0.04108457 max resid 0.06994378
*** No convergence -- monoMDS stopping criteria:
6: no. of iterations >= maxit
32: stress < smin
6: stress ratio > sratmax
6: scale factor of the gradient < sfgrmin
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0
Run 1 stress 9.27389e-05
... Procrustes: rmse 0.1521255 max resid 0.229653
Run 2 stress 8.678388e-05
... Procrustes: rmse 0.1457964 max resid 0.2075432
Run 3 stress 9.994686e-05
... Procrustes: rmse 0.1852294 max resid 0.2419772
Run 4 stress 9.199759e-05
... Procrustes: rmse 0.1373251 max resid 0.2063759
Run 5 stress 9.410999e-05
... Procrustes: rmse 0.1602437 max resid 0.2493081
Run 6 stress 8.60815e-05
... Procrustes: rmse 0.08846057 max resid 0.115434
Run 7 stress 0
... Procrustes: rmse 0.08295965 max resid 0.1061013
Run 8 stress 9.745948e-05
... Procrustes: rmse 0.136724 max resid 0.1960041
Run 9 stress 8.348885e-05
... Procrustes: rmse 0.05872036 max resid 0.0925466
Run 10 stress 9.957964e-05
... Procrustes: rmse 0.04630629 max resid 0.08244466
Run 11 stress 0
... Procrustes: rmse 0.1479404 max resid 0.2197014
Run 12 stress 8.530713e-05
... Procrustes: rmse 0.04799295 max resid 0.08505366
Run 13 stress 3.636391e-05
... Procrustes: rmse 0.1374507 max resid 0.2167818
Run 14 stress 0
... Procrustes: rmse 0.1631078 max resid 0.2339443
Run 15 stress 0.2181459
Run 16 stress 9.822635e-05
... Procrustes: rmse 0.1106336 max resid 0.2016868
Run 17 stress 7.288696e-05
... Procrustes: rmse 0.1673717 max resid 0.3042097
Run 18 stress 0.2280378
Run 19 stress 9.838413e-05
... Procrustes: rmse 0.1275469 max resid 0.1980726
Run 20 stress 9.786091e-05
... Procrustes: rmse 0.1871905 max resid 0.2572692
Run 21 stress 3.048397e-05
... Procrustes: rmse 0.1030804 max resid 0.1607529
Run 22 stress 7.114444e-05
... Procrustes: rmse 0.06017737 max resid 0.09396983
Run 23 stress 8.875821e-05
... Procrustes: rmse 0.124411 max resid 0.1675822
Run 24 stress 0
... Procrustes: rmse 0.1461035 max resid 0.2201612
Run 25 stress 0.2181459
Run 26 stress 0
... Procrustes: rmse 0.1679669 max resid 0.2317372
Run 27 stress 0.2761349
Run 28 stress 0
... Procrustes: rmse 0.1514277 max resid 0.2227922
Run 29 stress 0
... Procrustes: rmse 0.1564645 max resid 0.2247786
Run 30 stress 0
... Procrustes: rmse 0.1509248 max resid 0.2735906
Run 31 stress 7.888498e-05
... Procrustes: rmse 0.1511732 max resid 0.2622046
Run 32 stress 0
... Procrustes: rmse 0.1281077 max resid 0.2386671
Run 33 stress 8.469696e-05
... Procrustes: rmse 0.05575747 max resid 0.08619757
Run 34 stress 8.380588e-05
... Procrustes: rmse 0.1491894 max resid 0.2197302
Run 35 stress 9.966382e-05
... Procrustes: rmse 0.1693455 max resid 0.3146919
Run 36 stress 9.767769e-05
... Procrustes: rmse 0.1580556 max resid 0.2702835
Run 37 stress 9.754226e-05
... Procrustes: rmse 0.1790055 max resid 0.2677196
Run 38 stress 0
... Procrustes: rmse 0.128145 max resid 0.1878178
Run 39 stress 9.713453e-05
... Procrustes: rmse 0.184508 max resid 0.2408183
Run 40 stress 9.174066e-05
... Procrustes: rmse 0.2033799 max resid 0.3677587
Run 41 stress 9.740008e-05
... Procrustes: rmse 0.1122958 max resid 0.1478804
Run 42 stress 7.044335e-05
... Procrustes: rmse 0.116211 max resid 0.1389489
Run 43 stress 7.728447e-05
... Procrustes: rmse 0.184387 max resid 0.2490613
Run 44 stress 0.2269842
Run 45 stress 9.872794e-05
... Procrustes: rmse 0.1031123 max resid 0.1571142
Run 46 stress 9.134802e-05
... Procrustes: rmse 0.1772459 max resid 0.2465256
Run 47 stress 0.2143661
Run 48 stress 4.295714e-05
... Procrustes: rmse 0.04738141 max resid 0.0841658
Run 49 stress 8.764813e-05
... Procrustes: rmse 0.08365967 max resid 0.1113174
Run 50 stress 0.2269841
*** No convergence -- monoMDS stopping criteria:
43: stress < smin
4: stress ratio > sratmax
3: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0
Run 1 stress 3.044087e-06
... Procrustes: rmse 0.06945111 max resid 0.1122452
Run 2 stress 0.09533513
Run 3 stress 6.537764e-05
... Procrustes: rmse 0.1213111 max resid 0.1569751
Run 4 stress 0.1259703
Run 5 stress 0
... Procrustes: rmse 0.05565396 max resid 0.09658757
Run 6 stress 0.09533513
Run 7 stress 0
... Procrustes: rmse 0.09781367 max resid 0.1349073
Run 8 stress 0
... Procrustes: rmse 0.03404197 max resid 0.05373876
Run 9 stress 0
... Procrustes: rmse 0.07455857 max resid 0.1109817
Run 10 stress 9.185812e-10
... Procrustes: rmse 0.06654021 max resid 0.1076203
Run 11 stress 6.384493e-05
... Procrustes: rmse 0.05514556 max resid 0.08763858
Run 12 stress 8.005973e-05
... Procrustes: rmse 0.1231277 max resid 0.1965762
Run 13 stress 6.982077e-05
... Procrustes: rmse 0.02066864 max resid 0.02752758
Run 14 stress 0
... Procrustes: rmse 0.05616963 max resid 0.09282198
Run 15 stress 5.550769e-05
... Procrustes: rmse 0.06922159 max resid 0.1064621
Run 16 stress 7.00079e-05
... Procrustes: rmse 0.06424896 max resid 0.09448099
Run 17 stress 0
... Procrustes: rmse 0.05627784 max resid 0.08437025
Run 18 stress 8.530485e-05
... Procrustes: rmse 0.07086376 max resid 0.1132835
Run 19 stress 0
... Procrustes: rmse 0.05859341 max resid 0.08067741
Run 20 stress 0
... Procrustes: rmse 0.0406491 max resid 0.0564945
Run 21 stress 0
... Procrustes: rmse 0.06466189 max resid 0.08488737
Run 22 stress 2.681044e-05
... Procrustes: rmse 0.1207179 max resid 0.1820715
Run 23 stress 0
... Procrustes: rmse 0.1203763 max resid 0.1591241
Run 24 stress 0.125969
Run 25 stress 0
... Procrustes: rmse 0.05682729 max resid 0.08649165
Run 26 stress 6.470316e-08
... Procrustes: rmse 0.06771459 max resid 0.124488
Run 27 stress 6.32051e-05
... Procrustes: rmse 0.06896642 max resid 0.1094446
Run 28 stress 0
... Procrustes: rmse 0.07119419 max resid 0.1361896
Run 29 stress 3.063346e-10
... Procrustes: rmse 0.06904309 max resid 0.11099
Run 30 stress 0.09533513
Run 31 stress 0.125969
Run 32 stress 0.09533513
Run 33 stress 0
... Procrustes: rmse 0.06273515 max resid 0.09798401
Run 34 stress 9.571309e-05
... Procrustes: rmse 0.06157891 max resid 0.09631296
Run 35 stress 0.125969
Run 36 stress 0.09533513
Run 37 stress 0
... Procrustes: rmse 0.02611826 max resid 0.03955381
Run 38 stress 5.394408e-05
... Procrustes: rmse 0.07123217 max resid 0.1140157
Run 39 stress 6.281097e-06
... Procrustes: rmse 0.08023516 max resid 0.1538063
Run 40 stress 2.118811e-09
... Procrustes: rmse 0.07042497 max resid 0.1127274
Run 41 stress 0.09533513
Run 42 stress 0
... Procrustes: rmse 0.08976737 max resid 0.1146594
Run 43 stress 0
... Procrustes: rmse 0.04177211 max resid 0.06964027
Run 44 stress 0.2761348
Run 45 stress 8.809518e-05
... Procrustes: rmse 0.1260357 max resid 0.2157152
Run 46 stress 0
... Procrustes: rmse 0.0753679 max resid 0.1036992
Run 47 stress 0
... Procrustes: rmse 0.02392609 max resid 0.04095896
Run 48 stress 0
... Procrustes: rmse 0.1252648 max resid 0.2231107
Run 49 stress 0.09533513
Run 50 stress 0.1010567
*** No convergence -- monoMDS stopping criteria:
37: stress < smin
6: stress ratio > sratmax
7: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
[[1]]
[[1]][[1]]
[[1]][[1]]$x
[1] 0.006254553 0.029116235 0.209788654 0.280934147 0.281559959 0.030475264 0.208871595
[8] 0.278804603 0.279733500 0.207286778 0.266285549 0.269911406 0.150834932 0.146068287
[15] 0.021336136
[[1]][[1]]$y
[1] 0.003958143 0.038696260 0.409424620 0.546925832 0.547290722 0.040801233 0.406401320
[8] 0.545720228 0.545965978 0.404274865 0.521620989 0.522948826 0.295238280 0.282769190
[15] 0.017362503
[[1]][[1]]$yf
[1] 0.003958143 0.038696260 0.409424620 0.546925832 0.547290722 0.040801233 0.406401320
[8] 0.545720228 0.545965978 0.404274865 0.521620989 0.522948826 0.295238280 0.282769190
[15] 0.017362503
[[1]][[2]]
[[2]]
[[2]][[1]]
[[2]][[1]]$x
[1] 0.007527942 0.028883500 0.124233078 0.127565146 0.128719857 0.029900682 0.126736958
[8] 0.126156096 0.127319350 0.133097868 0.149405482 0.149215845 0.032682434 0.039362729
[15] 0.012821449
[[2]][[1]]$y
[1] 0.04051713 0.07833811 0.23820451 0.26675690 0.27491556 0.07863105 0.24357795 0.24287441
[9] 0.24585736 0.31530588 0.31890361 0.31780077 0.18506301 0.22600040 0.04387713
[[2]][[1]]$yf
[1] 0.04051713 0.07833811 0.23820451 0.26675690 0.27491556 0.07863105 0.24357795 0.24287441
[9] 0.24585736 0.31530588 0.31890361 0.31780077 0.18506301 0.22600040 0.04387713
[[2]][[2]]
[[3]]
[[3]][[1]]
[[3]][[1]]$x
[1] 0.22744092 0.23312397 0.22601228 0.01609754 0.01069745 0.02090434 0.03128485 0.21749839
[9] 0.22983959 0.05060696 0.22317568 0.23528745 0.21620894 0.22796633 0.02092702
[[3]][[1]]$y
[1] 0.449420251 0.462354040 0.447578162 0.027186246 0.007874912 0.029394970 0.066387340
[8] 0.422278681 0.456667117 0.094711652 0.435167874 0.469765678 0.420989785 0.454295726
[15] 0.034689145
[[3]][[1]]$yf
[1] 0.449420251 0.462354040 0.447578162 0.027186246 0.007874912 0.029394970 0.066387340
[8] 0.422278681 0.456667117 0.094711652 0.435167874 0.469765678 0.420989785 0.454295726
[15] 0.034689145
[[3]][[2]]
[[4]]
[[4]][[1]]
[[4]][[1]]$x
[1] 0.22155767 0.15323750 0.28820302 0.03427779 0.16545378 0.09748013 0.27884522 0.21610143
[9] 0.22830578 0.22614810 0.14960398 0.16283834 0.30771502 0.19371545 0.19243508
[[4]][[1]]$y
[1] 0.39630973 0.23733902 0.54180934 0.02265569 0.26045042 0.18410006 0.51266545 0.38885277
[9] 0.43211928 0.41636644 0.23709329 0.25689307 0.55759036 0.29855452 0.28003217
[[4]][[1]]$yf
[1] 0.39630973 0.23733902 0.54180934 0.02265569 0.26045042 0.18410006 0.51266545 0.38885277
[9] 0.43211928 0.41636644 0.23709329 0.25689307 0.55759036 0.29855452 0.28003217
[[4]][[2]]
# analyze sample F2_0
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F2_0")'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 7.188377e-05
Run 1 stress 0.2761351
Run 2 stress 0.2269841
Run 3 stress 8.101582e-05
... Procrustes: rmse 0.1378511 max resid 0.2526561
Run 4 stress 0
... New best solution
... Procrustes: rmse 0.1525377 max resid 0.226433
Run 5 stress 0.2029041
Run 6 stress 0.2269841
Run 7 stress 0
... Procrustes: rmse 0.08668495 max resid 0.1330141
Run 8 stress 2.207403e-05
... Procrustes: rmse 0.1064627 max resid 0.1864247
Run 9 stress 0
... Procrustes: rmse 0.1337557 max resid 0.2051906
Run 10 stress 1.753599e-06
... Procrustes: rmse 0.1385379 max resid 0.237886
Run 11 stress 0.2673072
Run 12 stress 0
... Procrustes: rmse 0.02836019 max resid 0.04187944
Run 13 stress 8.025176e-05
... Procrustes: rmse 0.0726192 max resid 0.09454725
Run 14 stress 0
... Procrustes: rmse 0.1502222 max resid 0.2265897
Run 15 stress 0
... Procrustes: rmse 0.1126678 max resid 0.1710298
Run 16 stress 0
... Procrustes: rmse 0.06281334 max resid 0.09094605
Run 17 stress 0
... Procrustes: rmse 0.1615955 max resid 0.2285288
Run 18 stress 0
... Procrustes: rmse 0.1450466 max resid 0.2852576
Run 19 stress 0
... Procrustes: rmse 0.04245243 max resid 0.06966204
Run 20 stress 0
... Procrustes: rmse 0.1325092 max resid 0.248607
Run 21 stress 0
... Procrustes: rmse 0.08177226 max resid 0.100589
Run 22 stress 0
... Procrustes: rmse 0.02510671 max resid 0.04018133
Run 23 stress 0
... Procrustes: rmse 0.1203113 max resid 0.1599242
Run 24 stress 0
... Procrustes: rmse 0.0500644 max resid 0.07866878
Run 25 stress 0
... Procrustes: rmse 0.06084285 max resid 0.1117558
Run 26 stress 0
... Procrustes: rmse 0.09056388 max resid 0.09650411
Run 27 stress 0.2181459
Run 28 stress 0
... Procrustes: rmse 0.02738887 max resid 0.04845312
Run 29 stress 0
... Procrustes: rmse 0.07434677 max resid 0.1257995
Run 30 stress 0
... Procrustes: rmse 0.08481603 max resid 0.109632
Run 31 stress 0
... Procrustes: rmse 0.1299555 max resid 0.2348551
Run 32 stress 0
... Procrustes: rmse 0.1188175 max resid 0.1970214
Run 33 stress 0
... Procrustes: rmse 0.1458604 max resid 0.2258245
Run 34 stress 0
... Procrustes: rmse 0.08512322 max resid 0.1248643
Run 35 stress 9.093154e-05
... Procrustes: rmse 0.1407966 max resid 0.2496436
Run 36 stress 0.2029041
Run 37 stress 0
... Procrustes: rmse 0.1180612 max resid 0.1947868
Run 38 stress 0.2672326
Run 39 stress 0
... Procrustes: rmse 0.08427885 max resid 0.1212251
Run 40 stress 0
... Procrustes: rmse 0.1243431 max resid 0.2343176
Run 41 stress 0
... Procrustes: rmse 0.06531926 max resid 0.1157826
Run 42 stress 0
... Procrustes: rmse 0.1126321 max resid 0.1569682
Run 43 stress 0
... Procrustes: rmse 0.07263194 max resid 0.1225922
Run 44 stress 9.321316e-05
... Procrustes: rmse 0.04294484 max resid 0.06979101
Run 45 stress 0
... Procrustes: rmse 0.05850514 max resid 0.09685507
Run 46 stress 0
... Procrustes: rmse 0.09218628 max resid 0.1481359
Run 47 stress 5.204586e-05
... Procrustes: rmse 0.1492392 max resid 0.2331048
Run 48 stress 0.2269841
Run 49 stress 0.2181459
Run 50 stress 0
... Procrustes: rmse 0.1642012 max resid 0.2604613
*** No convergence -- monoMDS stopping criteria:
40: stress < smin
7: stress ratio > sratmax
3: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient dataRun 0 stress 7.921199e-05
Run 1 stress 2.075768e-11
... New best solution
... Procrustes: rmse 0.1533861 max resid 0.2486492
Run 2 stress 0.1970957
Run 3 stress 0
... New best solution
... Procrustes: rmse 0.1416719 max resid 0.1714828
Run 4 stress 0
... Procrustes: rmse 0.1578028 max resid 0.2248607
Run 5 stress 0
... Procrustes: rmse 0.1204965 max resid 0.2121441
Run 6 stress 0.2269841
Run 7 stress 0
... Procrustes: rmse 0.1578575 max resid 0.2702088
Run 8 stress 9.602541e-05
... Procrustes: rmse 0.1293948 max resid 0.2358058
Run 9 stress 3.310182e-05
... Procrustes: rmse 0.1481482 max resid 0.2903267
Run 10 stress 0.2181459
Run 11 stress 0.2181459
Run 12 stress 0
... Procrustes: rmse 0.1646221 max resid 0.2684588
Run 13 stress 0
... Procrustes: rmse 0.1392139 max resid 0.1637873
Run 14 stress 0
... Procrustes: rmse 0.1121501 max resid 0.2043825
Run 15 stress 0
... Procrustes: rmse 0.1560801 max resid 0.1875962
Run 16 stress 0
... Procrustes: rmse 0.1463231 max resid 0.2808581
Run 17 stress 9.773829e-05
... Procrustes: rmse 0.1419352 max resid 0.2911118
Run 18 stress 0
... Procrustes: rmse 0.1905914 max resid 0.3381266
Run 19 stress 0
... Procrustes: rmse 0.2066693 max resid 0.3495548
Run 20 stress 9.807275e-05
... Procrustes: rmse 0.09424447 max resid 0.1587148
Run 21 stress 0.1967694
Run 22 stress 0
... Procrustes: rmse 0.1510066 max resid 0.2465763
Run 23 stress 4.133679e-05
... Procrustes: rmse 0.06409936 max resid 0.0891886
Run 24 stress 7.563102e-05
... Procrustes: rmse 0.1462 max resid 0.2622727
Run 25 stress 0
... Procrustes: rmse 0.1566686 max resid 0.2474357
Run 26 stress 1.172297e-05
... Procrustes: rmse 0.1545732 max resid 0.196343
Run 27 stress 0
... Procrustes: rmse 0.08942632 max resid 0.137192
Run 28 stress 8.807683e-05
... Procrustes: rmse 0.2048099 max resid 0.2671347
Run 29 stress 1.080862e-08
... Procrustes: rmse 0.1402199 max resid 0.2062496
Run 30 stress 0
... Procrustes: rmse 0.1223546 max resid 0.172425
Run 31 stress 9.185906e-05
... Procrustes: rmse 0.1150748 max resid 0.1763716
Run 32 stress 0
... Procrustes: rmse 0.1269937 max resid 0.1694881
Run 33 stress 0.2181459
Run 34 stress 0
... Procrustes: rmse 0.1509814 max resid 0.2046099
Run 35 stress 0
... Procrustes: rmse 0.06944273 max resid 0.09392927
Run 36 stress 0.2761385
Run 37 stress 0
... Procrustes: rmse 0.132748 max resid 0.239956
Run 38 stress 0.2672326
Run 39 stress 0
... Procrustes: rmse 0.1441822 max resid 0.2659112
Run 40 stress 0
... Procrustes: rmse 0.08825797 max resid 0.168706
Run 41 stress 0
... Procrustes: rmse 0.1446988 max resid 0.3042454
Run 42 stress 0
... Procrustes: rmse 0.06367835 max resid 0.1116393
Run 43 stress 0
... Procrustes: rmse 0.1144281 max resid 0.1867821
Run 44 stress 5.76876e-06
... Procrustes: rmse 0.0732613 max resid 0.1032739
Run 45 stress 0
... Procrustes: rmse 0.1324367 max resid 0.2025857
Run 46 stress 0
... Procrustes: rmse 0.1394626 max resid 0.222258
Run 47 stress 0
... Procrustes: rmse 0.1394865 max resid 0.2623241
Run 48 stress 0.2269841
Run 49 stress 0
... Procrustes: rmse 0.07662625 max resid 0.1104388
Run 50 stress 6.526912e-05
... Procrustes: rmse 0.1430293 max resid 0.2856894
*** No convergence -- monoMDS stopping criteria:
41: stress < smin
6: stress ratio > sratmax
3: scale factor of the gradient < sfgrmin
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 2.249929e-05
Run 1 stress 0.2761349
Run 2 stress 2.289779e-05
... Procrustes: rmse 0.1866514 max resid 0.2494207
Run 3 stress 9.333211e-05
... Procrustes: rmse 0.154691 max resid 0.3092455
Run 4 stress 0
... New best solution
... Procrustes: rmse 0.08838499 max resid 0.1462992
Run 5 stress 2.154406e-05
... Procrustes: rmse 0.1545686 max resid 0.2452741
Run 6 stress 9.832436e-05
... Procrustes: rmse 0.0754475 max resid 0.1522155
Run 7 stress 9.852451e-05
... Procrustes: rmse 0.09468275 max resid 0.1475169
Run 8 stress 8.484662e-05
... Procrustes: rmse 0.1130575 max resid 0.2311013
Run 9 stress 0
... Procrustes: rmse 0.1166551 max resid 0.179319
Run 10 stress 0.2269842
Run 11 stress 0.2269842
Run 12 stress 0
... Procrustes: rmse 0.1335803 max resid 0.2547946
Run 13 stress 9.268887e-05
... Procrustes: rmse 0.1385014 max resid 0.2180462
Run 14 stress 0
... Procrustes: rmse 0.07960846 max resid 0.1187512
Run 15 stress 9.436316e-05
... Procrustes: rmse 0.1021358 max resid 0.1608005
Run 16 stress 0
... Procrustes: rmse 0.0941109 max resid 0.1596651
Run 17 stress 0
... Procrustes: rmse 0.1556236 max resid 0.2814113
Run 18 stress 0
... Procrustes: rmse 0.07752391 max resid 0.1258718
Run 19 stress 7.960643e-05
... Procrustes: rmse 0.1297613 max resid 0.2015067
Run 20 stress 4.529276e-05
... Procrustes: rmse 0.1412719 max resid 0.2739843
Run 21 stress 9.414278e-05
... Procrustes: rmse 0.1699047 max resid 0.320062
Run 22 stress 0
... Procrustes: rmse 0.0969024 max resid 0.1752751
Run 23 stress 6.63577e-05
... Procrustes: rmse 0.1262935 max resid 0.254712
Run 24 stress 9.696424e-05
... Procrustes: rmse 0.1602765 max resid 0.3000258
Run 25 stress 0.2269842
Run 26 stress 0.2673072
Run 27 stress 0.2181459
Run 28 stress 0
... Procrustes: rmse 0.08365868 max resid 0.1325619
Run 29 stress 0.2761341
Run 30 stress 0.2181459
Run 31 stress 0
... Procrustes: rmse 0.07536874 max resid 0.1042113
Run 32 stress 0
... Procrustes: rmse 0.125977 max resid 0.2380113
Run 33 stress 0
... Procrustes: rmse 0.09280766 max resid 0.129987
Run 34 stress 0
... Procrustes: rmse 0.09076951 max resid 0.1739668
Run 35 stress 9.515772e-05
... Procrustes: rmse 0.1120239 max resid 0.1794874
Run 36 stress 8.828599e-05
... Procrustes: rmse 0.1514146 max resid 0.2422186
Run 37 stress 9.262122e-05
... Procrustes: rmse 0.1188812 max resid 0.1814029
Run 38 stress 0.2269841
Run 39 stress 0
... Procrustes: rmse 0.133943 max resid 0.2603554
Run 40 stress 0
... Procrustes: rmse 0.05248697 max resid 0.07462505
Run 41 stress 9.244271e-05
... Procrustes: rmse 0.1182056 max resid 0.1728993
Run 42 stress 8.949273e-05
... Procrustes: rmse 0.1406998 max resid 0.2721431
Run 43 stress 9.528495e-05
... Procrustes: rmse 0.1912779 max resid 0.3565254
Run 44 stress 9.271712e-05
... Procrustes: rmse 0.09126982 max resid 0.126712
Run 45 stress 0.2673072
Run 46 stress 9.246815e-05
... Procrustes: rmse 0.09829775 max resid 0.1586411
Run 47 stress 0
... Procrustes: rmse 0.117014 max resid 0.1731356
Run 48 stress 8.059002e-05
... Procrustes: rmse 0.07977944 max resid 0.1675462
Run 49 stress 7.023664e-05
... Procrustes: rmse 0.1024447 max resid 0.1708959
Run 50 stress 0.00108158
*** No convergence -- monoMDS stopping criteria:
1: no. of iterations >= maxit
39: stress < smin
7: stress ratio > sratmax
3: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
'csv_outputs/bray_dissimilar/mockfeeds' already exists'nperm' >= set of all permutations: complete enumeration.
Set of permutations < 'minperm'. Generating entire set.Run 0 stress 0
Run 1 stress 0.2761361
Run 2 stress 0.150716
Run 3 stress 0.1336414
Run 4 stress 8.382761e-05
... Procrustes: rmse 0.0568723 max resid 0.09393385
Run 5 stress 0.150716
Run 6 stress 0
... Procrustes: rmse 0.09999198 max resid 0.1378761
Run 7 stress 0
... Procrustes: rmse 0.116781 max resid 0.1636341
Run 8 stress 9.855595e-05
... Procrustes: rmse 0.1342877 max resid 0.1656941
Run 9 stress 9.946945e-05
... Procrustes: rmse 0.08573196 max resid 0.16378
Run 10 stress 7.003845e-05
... Procrustes: rmse 0.08585058 max resid 0.1639405
Run 11 stress 0.1506039
Run 12 stress 0
... Procrustes: rmse 0.1260673 max resid 0.1620707
Run 13 stress 8.562925e-05
... Procrustes: rmse 0.06288703 max resid 0.1125769
Run 14 stress 0.1066982
Run 15 stress 0
... Procrustes: rmse 0.1123722 max resid 0.1772052
Run 16 stress 0
... Procrustes: rmse 0.1229127 max resid 0.1741901
Run 17 stress 0.1336444
Run 18 stress 0.1066982
Run 19 stress 0.1336455
Run 20 stress 0
... Procrustes: rmse 0.0865444 max resid 0.1188462
Run 21 stress 0.1066982
Run 22 stress 7.487884e-05
... Procrustes: rmse 0.06640352 max resid 0.1192449
Run 23 stress 0.1506027
Run 24 stress 0.150603
Run 25 stress 0.2673072
Run 26 stress 0.2269841
Run 27 stress 0
... Procrustes: rmse 0.1232387 max resid 0.1502887
Run 28 stress 0
... Procrustes: rmse 0.1011606 max resid 0.1449335
Run 29 stress 0.1506028
Run 30 stress 0.2761355
Run 31 stress 0
... Procrustes: rmse 0.06900626 max resid 0.1135598
Run 32 stress 9.086962e-05
... Procrustes: rmse 0.1157697 max resid 0.1649378
Run 33 stress 0
... Procrustes: rmse 0.03331736 max resid 0.06808534
Run 34 stress 2.325576e-05
... Procrustes: rmse 0.01470123 max resid 0.02841497
Run 35 stress 8.916882e-05
... Procrustes: rmse 0.07114589 max resid 0.1251688
Run 36 stress 0
... Procrustes: rmse 0.07492711 max resid 0.1221508
Run 37 stress 0
... Procrustes: rmse 0.1078112 max resid 0.1395469
Run 38 stress 0.1506022
Run 39 stress 0.1336385
Run 40 stress 0
... Procrustes: rmse 0.1101607 max resid 0.1556894
Run 41 stress 0
... Procrustes: rmse 0.07371134 max resid 0.1049192
Run 42 stress 9.96391e-05
... Procrustes: rmse 0.07384028 max resid 0.1409495
Run 43 stress 9.391698e-05
... Procrustes: rmse 0.1257046 max resid 0.171469
Run 44 stress 0
... Procrustes: rmse 0.04399256 max resid 0.06262103
Run 45 stress 8.620591e-05
... Procrustes: rmse 0.08354666 max resid 0.1592804
Run 46 stress 0
... Procrustes: rmse 0.1151094 max resid 0.157271
Run 47 stress 0
... Procrustes: rmse 0.0875442 max resid 0.1675373
Run 48 stress 0.1506027
Run 49 stress 9.098117e-05
... Procrustes: rmse 0.1327475 max resid 0.1671193
Run 50 stress 0
... Procrustes: rmse 0.07512905 max resid 0.1449246
*** No convergence -- monoMDS stopping criteria:
31: stress < smin
18: stress ratio > sratmax
1: scale factor of the gradient < sfgrminstress is (nearly) zero: you may have insufficient data
[[1]]
[[1]][[1]]
[[1]][[1]]$x
[1] 0.009759950 0.012697374 0.106741779 0.107719610 0.107220948 0.018422623 0.113049520
[8] 0.114101491 0.113528689 0.100255074 0.101307045 0.100745589 0.002069468 0.003748236
[15] 0.002644918
[[1]][[1]]$y
[1] 0.10986126 0.11636147 0.20436522 0.22821371 0.21310424 0.12572227 0.25644523 0.28144853
[9] 0.27513739 0.13342851 0.15796416 0.15542623 0.02509673 0.02862736 0.02573959
[[1]][[1]]$yf
[1] 0.10986126 0.11636147 0.20436522 0.22821371 0.21310424 0.12572227 0.25644523 0.28144853
[9] 0.27513739 0.13342851 0.15796416 0.15542623 0.02509673 0.02862736 0.02573959
[[1]][[2]]
[[2]]
[[2]][[1]]
[[2]][[1]]$x
[1] 0.020268396 0.021856260 0.136126971 0.138332242 0.137020727 0.030853036 0.145359258
[8] 0.147578134 0.146326074 0.119078536 0.121406474 0.120094958 0.004083416 0.003552271
[15] 0.004815322
[[2]][[1]]$y
[1] 0.06539700 0.13583689 0.26472821 0.30771041 0.26770604 0.20038657 0.31788916 0.36226874
[9] 0.31939228 0.20752368 0.23897187 0.21556131 0.04530313 0.01121646 0.04773670
[[2]][[1]]$yf
[1] 0.06539700 0.13583689 0.26472821 0.30771041 0.26770604 0.20038657 0.31788916 0.36226874
[9] 0.31939228 0.20752368 0.23897187 0.21556131 0.04530313 0.01121646 0.04773670
[[2]][[2]]
[[3]]
[[3]][[1]]
[[3]][[1]]$x
[1] 0.014363096 0.017098114 0.103479974 0.105070305 0.102671486 0.015713784 0.102965706
[8] 0.104496389 0.102109929 0.089372677 0.090903360 0.088539120 0.003548708 0.013536289
[15] 0.012326219
[[3]][[1]]$y
[1] 0.07401970 0.08605525 0.22486407 0.23245617 0.21451979 0.07998893 0.22273181 0.22595666
[9] 0.20771160 0.14655891 0.15184406 0.13349611 0.01446419 0.01883708 0.01837334
[[3]][[1]]$yf
[1] 0.07401970 0.08605525 0.22486407 0.23245617 0.21451979 0.07998893 0.22273181 0.22595666
[9] 0.20771160 0.14655891 0.15184406 0.13349611 0.01446419 0.01883708 0.01837334
[[3]][[2]]
[[4]]
[[4]][[1]]
[[4]][[1]]$x
[1] 0.44920819 0.08412974 0.16748595 0.42854620 0.42488322 0.49129790 0.55417671 0.50243832
[9] 0.47076895 0.14165210 0.46357103 0.46259384 0.39364137 0.39806751 0.09785464
[[4]][[1]]$y
[1] 0.86126800 0.07032648 0.26368904 0.82129043 0.81034709 0.92533278 1.05716795 0.96744014
[9] 0.88266625 0.25214365 0.87256632 0.86659841 0.73646971 0.75581155 0.09301924
[[4]][[1]]$yf
[1] 0.86126800 0.07032648 0.26368904 0.82129043 0.81034709 0.92533278 1.05716795 0.96744014
[9] 0.88266625 0.25214365 0.87256632 0.86659841 0.73646971 0.75581155 0.09301924
[[4]][[2]]
Mock feed pools
# analyze the mock equal pool samples
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "MEP")'csv_outputs/bray_dissimilar/mockfeeds' already existsExpected 4 pieces. Missing pieces filled with `NA` in 9 rows [1, 2, 3, 4, 5, 6, 7, 8, 9].Run 0 stress 0.1055467
Run 1 stress 0.1082629
Run 2 stress 0.185943
Run 3 stress 0.1350568
Run 4 stress 0.1082629
Run 5 stress 0.1842716
Run 6 stress 0.145019
Run 7 stress 0.1450194
Run 8 stress 0.1540806
Run 9 stress 0.1055472
... Procrustes: rmse 0.0005453899 max resid 0.0009278687
... Similar to previous best
Run 10 stress 0.1350487
Run 11 stress 0.1082629
Run 12 stress 0.1450191
Run 13 stress 0.1055468
... Procrustes: rmse 8.963047e-05 max resid 0.0001364126
... Similar to previous best
Run 14 stress 0.2149903
Run 15 stress 0.1859443
Run 16 stress 0.137732
Run 17 stress 0.1540805
Run 18 stress 0.1082629
Run 19 stress 0.1082629
Run 20 stress 0.137732
*** Solution reached
Run 0 stress 0.1158061
Run 1 stress 0.169118
Run 2 stress 0.1964301
Run 3 stress 0.1646112
Run 4 stress 0.1691181
Run 5 stress 0.1158046
... New best solution
... Procrustes: rmse 0.0009669174 max resid 0.002062334
... Similar to previous best
Run 6 stress 0.1382198
Run 7 stress 0.2489173
Run 8 stress 0.2929585
Run 9 stress 0.1757775
Run 10 stress 0.1894733
Run 11 stress 0.1625856
Run 12 stress 0.1382199
Run 13 stress 0.1158048
... Procrustes: rmse 0.0002544148 max resid 0.0005257369
... Similar to previous best
Run 14 stress 0.1691182
Run 15 stress 0.1382243
Run 16 stress 0.1897223
Run 17 stress 0.1964301
Run 18 stress 0.1158048
... Procrustes: rmse 0.0002111826 max resid 0.0004449285
... Similar to previous best
Run 19 stress 0.169118
Run 20 stress 0.1382201
*** Solution reached
'csv_outputs/bray_dissimilar/mockfeeds' already existsExpected 4 pieces. Missing pieces filled with `NA` in 9 rows [1, 2, 3, 4, 5, 6, 7, 8, 9].Run 0 stress 0.1266085
Run 1 stress 0.1250975
... New best solution
... Procrustes: rmse 0.1116371 max resid 0.1714849
Run 2 stress 0.1237007
... New best solution
... Procrustes: rmse 0.1878832 max resid 0.3629085
Run 3 stress 0.1237007
... New best solution
... Procrustes: rmse 9.328018e-06 max resid 1.804131e-05
... Similar to previous best
Run 4 stress 0.1340103
Run 5 stress 0.1266086
Run 6 stress 0.142726
Run 7 stress 0.142726
Run 8 stress 0.1237007
... New best solution
... Procrustes: rmse 5.768097e-06 max resid 1.027572e-05
... Similar to previous best
Run 9 stress 0.1237007
... Procrustes: rmse 1.872509e-05 max resid 3.664431e-05
... Similar to previous best
Run 10 stress 0.1250975
Run 11 stress 0.2546845
Run 12 stress 0.3017341
Run 13 stress 0.1340063
Run 14 stress 0.1427261
Run 15 stress 0.2439358
Run 16 stress 0.17619
Run 17 stress 0.211868
Run 18 stress 0.1250976
Run 19 stress 0.142726
Run 20 stress 0.142726
*** Solution reached
'csv_outputs/bray_dissimilar/mockfeeds' already existsExpected 4 pieces. Missing pieces filled with `NA` in 9 rows [1, 2, 3, 4, 5, 6, 7, 8, 9].Run 0 stress 9.252521e-05
Run 1 stress 9.764809e-05
... Procrustes: rmse 2.100437e-05 max resid 3.82702e-05
... Similar to previous best
Run 2 stress 0.002703715
Run 3 stress 9.36114e-05
... Procrustes: rmse 3.36271e-06 max resid 4.621912e-06
... Similar to previous best
Run 4 stress 0.3017343
Run 5 stress 9.669343e-05
... Procrustes: rmse 1.858604e-05 max resid 3.408368e-05
... Similar to previous best
Run 6 stress 0.1089013
Run 7 stress 0.005645638
Run 8 stress 8.078914e-05
... New best solution
... Procrustes: rmse 4.373645e-05 max resid 7.176272e-05
... Similar to previous best
Run 9 stress 9.631914e-05
... Procrustes: rmse 5.622295e-05 max resid 9.228164e-05
... Similar to previous best
Run 10 stress 9.654678e-05
... Procrustes: rmse 5.534527e-05 max resid 9.380193e-05
... Similar to previous best
Run 11 stress 9.801693e-05
... Procrustes: rmse 7.706965e-05 max resid 0.0001305659
... Similar to previous best
Run 12 stress 9.609074e-05
... Procrustes: rmse 5.498922e-05 max resid 9.039059e-05
... Similar to previous best
Run 13 stress 8.184136e-05
... Procrustes: rmse 1.879096e-05 max resid 3.095525e-05
... Similar to previous best
Run 14 stress 9.902215e-05
... Procrustes: rmse 6.492789e-05 max resid 0.0001084927
... Similar to previous best
Run 15 stress 9.979534e-05
... Procrustes: rmse 6.527762e-05 max resid 0.0001107576
... Similar to previous best
Run 16 stress 9.590348e-05
... Procrustes: rmse 4.904905e-05 max resid 8.040971e-05
... Similar to previous best
Run 17 stress 9.643764e-05
... Procrustes: rmse 6.578904e-05 max resid 0.0001117304
... Similar to previous best
Run 18 stress 9.970145e-05
... Procrustes: rmse 6.924329e-05 max resid 0.0001305982
... Similar to previous best
Run 19 stress 0.1089011
Run 20 stress 9.763823e-05
... Procrustes: rmse 6.604179e-05 max resid 0.0001269389
... Similar to previous best
*** Solution reachedstress is (nearly) zero: you may have insufficient data
[[1]]
[[1]][[1]]
[[1]][[1]]$x
[1] 0.3357518 0.2700306 0.3595434 0.3959025 0.2204576 0.3101452 0.2962518 0.3627042 0.3426059
[10] 0.4577952 0.4882227 0.3241198 0.3621956 0.4440666 0.3942616 0.3017729 0.4115382 0.2685351
[19] 0.3521591 0.3325456 0.3534061 0.4205420 0.3640889 0.3631096 0.3920352 0.4236946 0.3844125
[28] 0.3600025 0.3515579 0.3938477 0.2922536 0.2993083 0.3530073 0.3647150 0.4454963 0.3201164
[[1]][[1]]$y
[1] 0.23617078 0.03762539 0.34190840 0.37359948 0.01694579 0.20414470 0.24583515 0.29980948
[9] 0.27107710 0.55632187 0.60388473 0.23595070 0.34105021 0.45112702 0.39758255 0.30475073
[17] 0.34558687 0.04763455 0.18524065 0.23232901 0.31380347 0.29502070 0.35187729 0.25040571
[25] 0.35937602 0.54779261 0.36999401 0.44130328 0.18271943 0.38441925 0.22091932 0.23567211
[33] 0.28303100 0.39018754 0.49904352 0.20926698
[[1]][[1]]$yf
[1] 0.23119612 0.04262997 0.33489542 0.36447080 0.01694579 0.23119612 0.23119612 0.33489542
[9] 0.23119612 0.55632187 0.60388473 0.23119612 0.33489542 0.49932105 0.36447080 0.23119612
[17] 0.36447080 0.04262997 0.23119612 0.23119612 0.31380347 0.36447080 0.35187729 0.33489542
[25] 0.36447080 0.49932105 0.36447080 0.33489542 0.23119612 0.36447080 0.22091932 0.23119612
[33] 0.28303100 0.36447080 0.49932105 0.23119612
[[1]][[2]]
[[2]]
[[2]][[1]]
[[2]][[1]]$x
[1] 0.3001029 0.3617612 0.3231230 0.2837100 0.3312523 0.3308951 0.2797711 0.3789591 0.2477878
[10] 0.2713214 0.2712994 0.2282147 0.2629750 0.2515766 0.2194640 0.2622830 0.3090623 0.3005126
[19] 0.2954255 0.2879893 0.2816027 0.2818472 0.2836117 0.3015214 0.2648673 0.3206234 0.2990247
[28] 0.2569708 0.2657031 0.3295128 0.2778473 0.2731560 0.2737862 0.3047030 0.2708267 0.2985946
[[2]][[1]]$y
[1] 0.24312184 0.31656928 0.21939429 0.17623836 0.31805430 0.29714638 0.15851120 0.37878522
[9] 0.12567039 0.10922138 0.12743570 0.08829063 0.11704342 0.08536120 0.13645681 0.10042195
[17] 0.24880845 0.16860369 0.22790244 0.17271791 0.15661511 0.19275500 0.19196326 0.22544634
[25] 0.09707505 0.21656858 0.16547388 0.12516108 0.09844085 0.23711611 0.07084277 0.16736313
[33] 0.07356731 0.16865990 0.13991114 0.22179775
[[2]][[1]]$yf
[1] 0.2030008 0.3173118 0.2282571 0.1834186 0.3173118 0.2971464 0.1575632 0.3787852 0.1082135
[10] 0.1147236 0.1147236 0.1082135 0.1082135 0.1082135 0.1082135 0.1082135 0.2282571 0.2030008
[19] 0.2030008 0.1834186 0.1575632 0.1834186 0.1834186 0.2030008 0.1082135 0.2282571 0.2030008
[28] 0.1082135 0.1082135 0.2371161 0.1147236 0.1147236 0.1147236 0.2030008 0.1147236 0.2030008
[[2]][[2]]
[[3]]
[[3]][[1]]
[[3]][[1]]$x
[1] 0.1568875 0.1725026 0.1703869 0.2279592 0.2005687 0.1908797 0.1812164 0.1429426 0.1614687
[10] 0.1792121 0.2154427 0.2359555 0.2102065 0.1645141 0.1308631 0.2506164 0.2169481 0.2526396
[19] 0.2580612 0.2496731 0.2400724 0.2143282 0.1626815 0.2016789 0.2354556 0.2063095 0.1888897
[28] 0.2131539 0.2534434 0.2537964 0.2192287 0.2486716 0.2604261 0.2585177 0.2358648 0.1542377
[[3]][[1]]$y
[1] 0.05722627 0.17705910 0.11177962 0.15447008 0.18579931 0.16829387 0.14826053 0.10793710
[9] 0.13445459 0.16788148 0.16788748 0.24158506 0.21069160 0.15872948 0.09819499 0.28412650
[17] 0.16229875 0.32890442 0.25743426 0.28894077 0.22271158 0.20661850 0.10286682 0.15284644
[25] 0.16305111 0.16997122 0.20130328 0.10645505 0.30261262 0.25777602 0.10387805 0.26588428
[33] 0.26828145 0.29619015 0.27357450 0.06820003
[[3]][[1]]$yf
[1] 0.0828896 0.1644004 0.1352546 0.1656588 0.1656588 0.1656588 0.1644004 0.0828896 0.1186607
[10] 0.1644004 0.1656588 0.2459570 0.1656588 0.1352546 0.0828896 0.2855333 0.1656588 0.2855333
[19] 0.2855333 0.2855333 0.2459570 0.1656588 0.1186607 0.1656588 0.1656588 0.1656588 0.1656588
[28] 0.1656588 0.2855333 0.2855333 0.1656588 0.2658843 0.2855333 0.2855333 0.2459570 0.0828896
[[3]][[2]]
[[4]]
[[4]][[1]]
[[4]][[1]]$x
[1] 0.1429239 0.1387844 0.2218590 0.1182296 0.1468817 0.1466908 0.3402315 0.2890817 0.1292108
[10] 0.1932092 0.1286691 0.1423629 0.1271427 0.3063619 0.2253164 0.2528256 0.1504117 0.1307400
[19] 0.1469667 0.3356008 0.2447417 0.2383977 0.2819196 0.2676795 0.3348372 0.3290096 0.1647244
[28] 0.1077739 0.3340839 0.2810676 0.1254004 0.3455767 0.2520377 0.2833718 0.2940115 0.4259479
[[4]][[1]]$y
[1] 1.115306e-04 9.268979e-05 3.474377e-01 3.408479e-05 7.321045e-05 6.971079e-05
[7] 3.476318e-01 3.474682e-01 6.580936e-05 3.473306e-01 7.746011e-05 1.093834e-04
[13] 9.546780e-05 3.476051e-01 3.473880e-01 3.473809e-01 6.885422e-05 5.060869e-05
[19] 1.201478e-04 3.476669e-01 3.473764e-01 3.474048e-01 3.474316e-01 3.473861e-01
[25] 3.476889e-01 3.476699e-01 6.965244e-05 5.850991e-05 3.476229e-01 3.474443e-01
[31] 1.280862e-04 3.476919e-01 3.474019e-01 3.475655e-01 3.474830e-01 6.021884e-01
[[4]][[1]]$yf
[1] 8.939744e-05 8.939744e-05 3.473965e-01 4.629735e-05 8.939744e-05 8.939744e-05
[7] 3.476625e-01 3.475056e-01 8.348643e-05 3.473306e-01 8.348643e-05 8.939744e-05
[13] 8.348643e-05 3.476051e-01 3.473965e-01 3.473965e-01 8.939744e-05 8.348643e-05
[19] 8.939744e-05 3.476625e-01 3.473965e-01 3.473965e-01 3.474379e-01 3.473965e-01
[25] 3.476625e-01 3.476464e-01 8.939744e-05 4.629735e-05 3.476464e-01 3.474379e-01
[31] 8.348643e-05 3.476919e-01 3.473965e-01 3.475056e-01 3.475056e-01 6.021884e-01
[[4]][[2]]
# analyze the mock feed pool
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "MFP")'csv_outputs/bray_dissimilar/mockfeeds' already existsExpected 4 pieces. Missing pieces filled with `NA` in 9 rows [1, 2, 3, 4, 5, 6, 7, 8, 9].Run 0 stress 0.06364928
Run 1 stress 0.09471005
Run 2 stress 0.2265825
Run 3 stress 0.06365546
... Procrustes: rmse 0.002148102 max resid 0.004271723
... Similar to previous best
Run 4 stress 0.0939424
Run 5 stress 0.06365676
... Procrustes: rmse 0.002442423 max resid 0.004857421
... Similar to previous best
Run 6 stress 0.2222792
Run 7 stress 0.06365594
... Procrustes: rmse 0.005796783 max resid 0.01152563
Run 8 stress 0.06365171
... Procrustes: rmse 0.0009778457 max resid 0.001954567
... Similar to previous best
Run 9 stress 0.06365912
... Procrustes: rmse 0.002955246 max resid 0.005864934
... Similar to previous best
Run 10 stress 0.06365587
... Procrustes: rmse 0.002217254 max resid 0.004414995
... Similar to previous best
Run 11 stress 0.09394542
Run 12 stress 0.210603
Run 13 stress 0.09548629
Run 14 stress 0.2040939
Run 15 stress 0.2222793
Run 16 stress 0.09394843
Run 17 stress 0.09464587
Run 18 stress 0.09470022
Run 19 stress 0.0947082
Run 20 stress 0.06365309
... Procrustes: rmse 0.001351187 max resid 0.002702174
... Similar to previous best
*** Solution reached
Run 0 stress 0.1248886
Run 1 stress 0.09366553
... New best solution
... Procrustes: rmse 0.1997326 max resid 0.4466437
Run 2 stress 0.2590489
Run 3 stress 0.1443647
Run 4 stress 0.1248886
Run 5 stress 0.116627
Run 6 stress 0.1248886
Run 7 stress 0.1443647
Run 8 stress 0.1166312
Run 9 stress 0.09366558
... Procrustes: rmse 0.0002539039 max resid 0.0004955897
... Similar to previous best
Run 10 stress 0.146347
Run 11 stress 0.1248886
Run 12 stress 0.1166334
Run 13 stress 0.160528
Run 14 stress 0.116625
Run 15 stress 0.1443647
Run 16 stress 0.1166264
Run 17 stress 0.1167294
Run 18 stress 0.1166291
Run 19 stress 0.0936655
... New best solution
... Procrustes: rmse 6.323389e-05 max resid 0.0001308487
... Similar to previous best
Run 20 stress 0.09366551
... Procrustes: rmse 0.0001062808 max resid 0.0001854301
... Similar to previous best
*** Solution reached
'csv_outputs/bray_dissimilar/mockfeeds' already existsExpected 4 pieces. Missing pieces filled with `NA` in 9 rows [1, 2, 3, 4, 5, 6, 7, 8, 9].Run 0 stress 0.07183397
Run 1 stress 0.07183397
... Procrustes: rmse 7.598492e-06 max resid 1.485332e-05
... Similar to previous best
Run 2 stress 0.1292396
Run 3 stress 0.07183397
... Procrustes: rmse 9.071516e-06 max resid 1.646231e-05
... Similar to previous best
Run 4 stress 0.07183397
... Procrustes: rmse 2.366477e-06 max resid 4.246065e-06
... Similar to previous best
Run 5 stress 0.1292396
Run 6 stress 0.1292398
Run 7 stress 0.07183397
... Procrustes: rmse 1.113012e-05 max resid 1.974735e-05
... Similar to previous best
Run 8 stress 0.07183397
... Procrustes: rmse 4.089457e-06 max resid 7.360054e-06
... Similar to previous best
Run 9 stress 0.07183398
... Procrustes: rmse 1.208527e-05 max resid 2.536354e-05
... Similar to previous best
Run 10 stress 0.1292396
Run 11 stress 0.07183397
... Procrustes: rmse 3.958695e-06 max resid 7.239967e-06
... Similar to previous best
Run 12 stress 0.07183397
... Procrustes: rmse 6.767628e-06 max resid 1.107656e-05
... Similar to previous best
Run 13 stress 0.07183397
... Procrustes: rmse 6.732769e-06 max resid 1.143215e-05
... Similar to previous best
Run 14 stress 0.07183397
... Procrustes: rmse 2.185775e-06 max resid 4.154859e-06
... Similar to previous best
Run 15 stress 0.07183397
... Procrustes: rmse 1.171993e-06 max resid 2.293682e-06
... Similar to previous best
Run 16 stress 0.07183397
... New best solution
... Procrustes: rmse 1.10235e-06 max resid 2.079438e-06
... Similar to previous best
Run 17 stress 0.1073256
Run 18 stress 0.2250754
Run 19 stress 0.1073257
Run 20 stress 0.07183397
... Procrustes: rmse 4.53578e-06 max resid 7.85255e-06
... Similar to previous best
*** Solution reached
'csv_outputs/bray_dissimilar/mockfeeds' already existsExpected 4 pieces. Missing pieces filled with `NA` in 9 rows [1, 2, 3, 4, 5, 6, 7, 8, 9].Run 0 stress 0.001975787
Run 1 stress 0.003833382
Run 2 stress 0.002294091
... Procrustes: rmse 0.02957969 max resid 0.06563765
Run 3 stress 0.002570182
Run 4 stress 0.0003577373
... New best solution
... Procrustes: rmse 0.02727485 max resid 0.04194552
Run 5 stress 0.001907351
Run 6 stress 0.0007806292
... Procrustes: rmse 0.003996374 max resid 0.006402149
... Similar to previous best
Run 7 stress 0.0003135241
... New best solution
... Procrustes: rmse 0.0002299896 max resid 0.0004166386
... Similar to previous best
Run 8 stress 0.3018151
Run 9 stress 9.728205e-05
... New best solution
... Procrustes: rmse 0.001534237 max resid 0.002814251
... Similar to previous best
Run 10 stress 9.758163e-05
... Procrustes: rmse 0.0002172372 max resid 0.0003445726
... Similar to previous best
Run 11 stress 0.003105635
Run 12 stress 0.000384661
... Procrustes: rmse 0.001900999 max resid 0.003482793
... Similar to previous best
Run 13 stress 9.675326e-05
... New best solution
... Procrustes: rmse 0.0002157398 max resid 0.0003438748
... Similar to previous best
Run 14 stress 0.004260075
Run 15 stress 0.002718237
Run 16 stress 0.001901804
Run 17 stress 0.004203702
Run 18 stress 0.00262929
Run 19 stress 0.001750504
Run 20 stress 0.0002349262
... Procrustes: rmse 0.001101485 max resid 0.002134027
... Similar to previous best
*** Solution reachedstress is (nearly) zero: you may have insufficient data
[[1]]
[[1]][[1]]
[[1]][[1]]$x
[1] 0.12969477 0.10958570 0.11771040 0.09601389 0.09756598 0.13174901 0.11472929 0.11192856
[9] 0.12277036 0.10408660 0.12633721 0.13398938 0.10489474 0.12217917 0.14687359 0.08830671
[17] 0.11286614 0.09693293 0.11473830 0.09698764 0.12180394 0.11331828 0.09577356 0.10010005
[25] 0.10735922 0.12472923 0.09867822 0.13710163 0.10325033 0.12304605 0.12711871 0.11815996
[33] 0.11682005 0.12845388 0.15710990 0.13305873
[[1]][[1]]$y
[1] 0.11292424 0.04934745 0.06291186 0.02438304 0.04071540 0.11617164 0.06182837 0.04866942
[9] 0.07178118 0.05985609 0.09957791 0.10116970 0.04911517 0.06541828 0.15378851 0.01393776
[17] 0.04999054 0.02987397 0.06683967 0.05176084 0.08271589 0.06086888 0.04147635 0.05336148
[25] 0.05351196 0.09612144 0.05544973 0.11285573 0.04056242 0.07267455 0.08791601 0.07479811
[33] 0.05652938 0.09313540 0.14441624 0.11001424
[[1]][[1]]$yf
[1] 0.11006996 0.05174844 0.06291186 0.03191112 0.04623812 0.11006996 0.06173248 0.05174844
[9] 0.07347760 0.05174844 0.09418769 0.11006996 0.05174844 0.07347760 0.14910237 0.01393776
[17] 0.05174844 0.03191112 0.06173248 0.04623812 0.07347760 0.06086888 0.03191112 0.04979121
[25] 0.05174844 0.09418769 0.04979121 0.11285573 0.04979121 0.07347760 0.09418769 0.07347760
[33] 0.06173248 0.09418769 0.14910237 0.11006996
[[1]][[2]]
[[2]]
[[2]][[1]]
[[2]][[1]]$x
[1] 0.09486471 0.12097318 0.10099228 0.09409658 0.11593523 0.07340183 0.09927172 0.08903083
[9] 0.10649828 0.10633260 0.10587594 0.11119368 0.09594250 0.10110508 0.08728567 0.08755330
[17] 0.09610044 0.09601493 0.11497171 0.11544096 0.09997613 0.09434976 0.09337393 0.10094043
[25] 0.10633563 0.08883171 0.11110650 0.08917919 0.09426727 0.08262075 0.10763045 0.09980406
[33] 0.10679740 0.08834883 0.07893388 0.08872040
[[2]][[1]]$y
[1] 0.03149158 0.08483143 0.06457228 0.03954221 0.08896503 0.02039573 0.04863811 0.02830449
[9] 0.06585594 0.05691991 0.04825077 0.08954277 0.04576129 0.07005559 0.03398557 0.03069606
[17] 0.06072833 0.05527719 0.08317187 0.08740105 0.06042096 0.03211008 0.03500635 0.05734267
[25] 0.05711761 0.03666610 0.04958664 0.02646602 0.02833983 0.01449590 0.07489700 0.06004582
[33] 0.06153742 0.02826155 0.02294621 0.03805146
[[2]][[1]]$yf
[1] 0.03329801 0.08706584 0.05938323 0.03329801 0.08706584 0.01927928 0.05488121 0.03237202
[9] 0.06296925 0.05938323 0.05938323 0.08635732 0.04576129 0.05938323 0.03098106 0.03098106
[17] 0.05488121 0.05488121 0.08635732 0.08706584 0.05926982 0.03329801 0.03329801 0.05926982
[25] 0.05938323 0.03237202 0.06296925 0.03237202 0.03329801 0.01927928 0.06296925 0.05926982
[33] 0.06296925 0.03098106 0.01927928 0.03237202
[[2]][[2]]
[[3]]
[[3]][[1]]
[[3]][[1]]$x
[1] 0.07801445 0.07952722 0.09199279 0.09242663 0.10324405 0.05060173 0.09632989 0.12259860
[9] 0.10755675 0.09044303 0.07068372 0.06981024 0.08297019 0.08423405 0.08594460 0.05229529
[17] 0.09509476 0.11504447 0.09231108 0.10967240 0.13449489 0.08383343 0.09845069 0.08790914
[25] 0.10072986 0.12808187 0.09863779 0.07245620 0.05435074 0.09011568 0.11549493 0.12276820
[33] 0.07693811 0.07929081 0.13089619 0.10626162
[[3]][[1]]$y
[1] 0.08946311 0.06846987 0.07247073 0.06839914 0.14115901 0.01401325 0.08867140 0.16192811
[9] 0.12166117 0.07900667 0.03880938 0.06023132 0.09077463 0.09219430 0.07760127 0.05140065
[17] 0.12082948 0.15043037 0.08227566 0.15529130 0.19907433 0.09182617 0.09920627 0.08469973
[25] 0.13950298 0.15374956 0.09890105 0.06419961 0.05495053 0.09560544 0.14581029 0.15048328
[33] 0.07864038 0.07665928 0.15952390 0.12360090
[[3]][[1]]$yf
[1] 0.07830816 0.07830816 0.08348537 0.08348537 0.13148102 0.01401325 0.10190205 0.15538698
[9] 0.13148102 0.08348537 0.05134797 0.05134797 0.08348537 0.08348537 0.08348537 0.05134797
[17] 0.10190205 0.15051065 0.08348537 0.15051065 0.19907433 0.08348537 0.10190205 0.08348537
[25] 0.13148102 0.15538698 0.10190205 0.06419961 0.05134797 0.08348537 0.15051065 0.15538698
[33] 0.07830816 0.07830816 0.15952390 0.13148102
[[3]][[2]]
[[4]]
[[4]][[1]]
[[4]][[1]]$x
[1] 0.21565519 0.20559776 0.14253007 0.82105694 0.23074602 0.20483653 0.81896184 0.79134458
[9] 0.09648571 0.15369417 0.91290869 0.15670174 0.22512511 0.91700536 0.88691019 0.11979673
[17] 0.87475314 0.12621099 0.19020092 0.88212332 0.85369718 0.81560429 0.14231421 0.16141021
[25] 0.81661512 0.80078055 0.86433000 0.75725644 0.11975569 0.23404484 0.25214280 0.86669936
[33] 0.83893795 0.77207372 0.73956678 0.28952762
[[4]][[1]]$y
[1] 0.0007772069 0.0006471966 0.0004930579 1.4266836205 0.0009629720 0.0007346404
[7] 1.4266844212 1.4261657898 0.0001657616 0.0005816072 1.4272819056 0.0004711314
[13] 0.0009490138 1.4272829564 1.4267642009 0.0004167141 1.4271168474 0.0004241873
[19] 0.0007898603 1.4271178905 1.4265991388 1.4267003072 0.0005242190 0.0003887358
[25] 1.4267013565 1.4261826018 1.4270343661 1.4263458865 0.0007196787 0.0006320038
[31] 0.0007305160 1.4270356191 1.4265167633 1.4263470163 1.4258282216 0.0006297504
[[4]][[1]]$yf
[1] 0.0007772069 0.0007238991 0.0005024164 1.4266476013 0.0007808512 0.0007238991
[7] 1.4266476013 1.4262603236 0.0001657616 0.0005024164 1.4272819056 0.0005024164
[13] 0.0007808512 1.4272829564 1.4270137848 0.0005024164 1.4270137848 0.0005024164
[19] 0.0007238991 1.4270137848 1.4266476013 1.4266476013 0.0005024164 0.0005024164
[25] 1.4266476013 1.4262603236 1.4270137848 1.4262603236 0.0005024164 0.0007808512
[31] 0.0007808512 1.4270137848 1.4266476013 1.4262603236 1.4258282216 0.0007808512
[[4]][[2]]
Crack open the bray dissimilarity files to take a look at anything > 0.49 dissimilar from the other replicates.
MFP: fishminiA - 36 entries - remove three replicates (see below)
F2_0 fishminiA - 6 entries (F2_0_1_3) F1_2 fishminiA - 8 entries (F1_2_2_3) F0_100 fishminiA - 18 entries F0_100 nsCOIFo - 6 entries (F0_100_1_1) F0_100 mifish - 18 entries F0_100 cep - 2 entries (F0_100_2_2 and F0_100_1_1) F1_0 fishminiA - 16 entries (F0_0_1_1 and F0_0_1_2)
I hate the idea of doing this manually, but for the time being, it is nice to have eyes on some of these data before dropping replicates.
Interesting to note that FishminiA has the highest incidence of dissimilarity issues.
Remove dissimilar replicates
# this is the dataframe that have been filtered by occupancy modeling and includes only those ASVs assigned to metazoan taxonomy
mock_feed_sodm_asvs_removed_dataframeNAA clean, non-redundant version of the reference sample dataframe
mockfeed_sodm_filtered_unique <- mock_feed_sodm_asvs_removed_dataframe %>%
select(locus, seq, sample, count) %>%
unique() %>% # if there are multiple entries with different counts, we want to collapse those reads
group_by(locus, seq, sample) %>%
mutate(total_reads = sum(count)) %>%
select(-count) %>%
rename(count = total_reads)
So that is the dataframe from which we want to remove this particular list of locus-samples
# read in the list of samples to remove
mockfeed_tossers <- read_csv("../data/mock_feed_replicates_to_remove.csv")Parsed with column specification:
cols(
locus = [31mcol_character()[39m,
sample = [31mcol_character()[39m
)It turns out that an anti-join is all I need for this filtering step.
mockfeed_sodm_bray_filtered_unique <- mockfeed_sodm_filtered_unique %>%
anti_join(., mockfeed_tossers, by = c("locus", "sample"))
# remove the negative controls from this df
mockfeed_decontaminated <- mockfeed_sodm_bray_filtered_unique %>%
filter(!str_detect(sample, "NEG") & !str_detect(sample, "M_A")) # remove positive controls tooSave that
saveRDS(mockfeed_decontaminated, "../data/mockfeed_decontaminated.rds", compress = "xz")Okay, so that is the dataset that I can work through the assessment analyses with, beginning with summary statistics, then adding in the taxonomy and assessing proportion fishmeal and % reads, etc.
Taxonomy for those ASVs retained
# un-collapsed and collapsed versions of the taxonomy with a 98% identity threshold for species-level ID
tax_df_clean_slim <- readRDS("../data/uncollapsed_taxonomy_spp_98.rds")
clean_taxonomy_df_unique <- readRDS("../data/unique_taxonomy_spp_98.rds")
Adding back in the taxonomic information - uncollapsed, so there are multiple taxonomic entries for each locus/seq that need to be summarised.
# bind the complete (but filtered) taxonomic identities onto the ASV sequence information
mockfeed_taxonomy <- mockfeed_decontaminated %>%
left_join(., tax_df_clean_slim, by = c("locus", "seq"))
Save that
# save output dataframe that has been filtered by Bray-Curtis as well as the occupancy modeling
saveRDS(mockfeed_taxonomy, "../data/mockfeed_taxonomy_sodm_bray_clean.rds", compress = "xz")
---
title: "Mock feed data - read in occupany modeling results and filter by Bray-Curtis"
output: html_notebook
---

The data decontamination includes two steps:
1. species occupancy detection modeling
2. Bray-Curtis dissimilarity analysis

Here, I'll read in the data from the SODM analysis, assess which ASVs will be removed based on that, and then move onto calculating the Bray-Curtis dissimilarities across sample replicates. Finally, the repicates that are more dissimilar (> 0.49) than similar to one another will be removed.


## Load data and libraries

```{r load-libraries}
library(tidyverse)
library(stringi)
library(vegan)
library(reshape2)
library(textshape)
library(rlist)

```

As I did with the reference mock communities, the way I'm thinking about this is to create a .txt file with a list of the file names and loci and to read that in, but ideally, I would also have a column for the sample name so that I could actually properly filter for each locus-sample when anti-joining with the dataframe.

Before getting into all of that regex stuff, probably best to take a quick look at whether there are many ASVs that get removed for the data from F1 or the mock feed pools.

```{r load-dataframes}
# Full dataframe
features_w_taxonomy <- readRDS("../data/feature_df_no_eDNA_metazoa_only.rds")

# and newly updated, the same dataframes, but using a 98% identity threshold for any species-level assignments
tax_df_spp_98 <- readRDS("../data/uncollapsed_taxonomy_spp_98.rds")
unique_tax_spp_98 <- readRDS("../data/unique_taxonomy_spp_98.rds")


# meta data for group and type assignments
meta <- read_csv("../data/jan_sample_list.csv")

# use the new name column to bind to the input file
feature_tax_name_revised <- features_w_taxonomy %>%
  left_join(., meta, by = "sample") %>%
  select(locus, seq, new_name, count) %>%
  rename(sample = new_name)


```


## Occupancy modeling data

Based on the SODM filtering, now I need to read those data back in for the mock feed data/mixtures and pools.

The files are located in this directory:
/Users/dianabaetscher/Documents/git-repos/metabarcoding-methods/Rmd_fixed_db/csv_outputs/SODM


```{r create-dataframe-from-csvs}
# get the names of the files
fdf <- read.table("~/Documents/git-repos/metabarcoding-methods/data/sodm_samples_loc.txt", stringsAsFactors = FALSE, header = TRUE) %>%
  tbl_df()
dir <- "~/Documents/git-repos/metabarcoding-methods/Rmd_fixed_db/csv_outputs/SODM"

# cycle over them, read them and add the locus column on each.
# at the end, bind them together.
mock_feed_sodm_df <- lapply(1:nrow(fdf), function(i) {
  read_csv(paste(dir, fdf$file[i], sep = "/"), col_names = TRUE) %>%
    mutate(locus = fdf$locus[i]) %>%
    mutate(sample = fdf$sample[i]) %>%
    select(locus, sample, everything())
}) %>%
  bind_rows() %>%
  group_by(locus, sample, seq) %>%
  mutate(max_estimate = sum(estimate,std.error)) %>%
  mutate(max_estimate = ifelse(max_estimate > 1, 1, max_estimate)) %>% # make 1 the maximum probability.
  select(locus, seq, estimate, std.error, max_estimate)

```


So these are the ASVs that I would be filtering
```{r filter-asvs-by-sodm}
mock_feed_sodm_asvs_removed <- mock_feed_sodm_df %>%
  filter(max_estimate < 0.8)

mock_feed_sodm_asvs_removed
```
There aren't very many of these, but some...

so for consistency sake, I suppose better to remove them.


Anti-join the ASVs to remove with the full dataframe
```{r}
mock_feed_feature_df <- feature_tax_name_revised %>%
  filter(!str_detect(sample, "VRP") & !str_detect(sample, "FRP") & !str_detect(sample, "extr_blank")) %>%
  separate(sample, into = c("filler", "perc_fishmeal", "extract", "pcr_rep"), sep = "_", remove = FALSE) %>%
  unite(feed, filler, perc_fishmeal, sep = "_", remove = TRUE) %>%
  select(-extract, -pcr_rep) %>%
  mutate(feed = ifelse(str_detect(feed, "MFP"), "MFP", feed)) %>%
  mutate(feed = ifelse(str_detect(feed, "MEP"), "MEP", feed))

# check that all of those modifications took effect - basically to make a column that I can use for an anti-join with the SODM filtered data
mock_feed_feature_df %>%
  filter(str_detect(sample, "MFP"))
```

Use an anti-join to remove the 27 ASVs from the full feature table.
```{r}
mock_feed_sodm_asvs_removed_dataframe <- mock_feed_feature_df %>%
  anti_join(., mock_feed_sodm_asvs_removed, by = c("locus", "seq", "feed" = "sample")) %>%
  select(-feed) # remove the column that I used for the join since I don't need it moving forward

```

Save the output
```{r save-output-rds}
saveRDS(mock_feed_sodm_asvs_removed_dataframe, "../data/mockfeed_sodm_asvs_removed_feature_df.rds", compress = "xz")

```



## Dissimilarity analyses


Using the dataframe generated above...

Dissimilarity analyses include a PERMANOVA on Bray-Curtis
Jaccard similarities (minimizing the effect of PCR amplification bias)


The process for looking at the dissimilarity among replicates is to:
1. read in data that has been cleaned up using the occupancy modeling
2. create a community matrix (per locus)
3. standardize data across replicates (fct decostand)
4. generate Bray-Curtis distances (fct vegdist)
   4a. Are any replicates more dissimilar than similar? 
5. generate NMDS plots from distance matrix (fct metaMDS)



Outcomes:
Based on the NMDS plots and Bray-Curtis dissimilarity index, I will generate a list of samples to remove.


```{r load-functions}
source("../R/metabarcoding-funcs.R")
```


Cycle over a list of the loci
```{r locus-list}
# grab the names of the loci from the full dataframe
locs <- c("cep", "mifish", "nsCOIFo", "fishminiA")

# how many feed samples?
mock_feed_sodm_asvs_removed_dataframe %>%
  select(sample) %>%
  unique()

```

NOTE: MEP and MFP have a diff number of parts than the mock feeds... 9 replicates vs. 6 replicates,
so splitting the sample into reference, pool, and pcr replicate will look different.


## Bray-Curtis function

Output from the function should go into a directory called
`csv_outputs/bray_dissimilar/mockfeeds/`

If this directory doesn't exist, the function will create it.
```{r}
bray_nmds_mock_feed(mock_feed_sodm_asvs_removed_dataframe, loc = "cep", site = "F1_0")

```

```{r 100%-fishmeal-feed}
# analyze the four loci for mock feed sample F0_100
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F0_100")
```

#### Filler 1

```{r 0%-fishmeal-filler1}
# lapply the function with all four loci
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F1_0")

```

```{r 10%-fishmeal-filler1}
# analyze sample F1_10
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F1_10")

```

```{r 2%-fishmeal-filler1}
# analyze sample F1_2
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F1_2_")

```


```{r 25%-fishmeal-filler1}
# analyze sample F1_25
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F1_25")

```


#### Filler 2

```{r 25%-fishmeal-filler2}
# analyze sample F2_25
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F2_25")

```

```{r 0%-fishmeal-filler2}
# analyze sample F2_0
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "F2_0")

```



## Mock feed pools


```{r mock-equal-pool}
# analyze the mock equal pool samples
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "MEP")

```

```{r mock-feed-pool}
# analyze the mock feed pool
lapply(locs, bray_nmds_mock_feed, sodm_filtered_df = mock_feed_sodm_asvs_removed_dataframe, site = "MFP")

```

Crack open the bray dissimilarity files to take a look at anything > 0.49 dissimilar from the other replicates.

MFP: fishminiA - 36 entries - remove three replicates (see below)

F2_0 fishminiA - 6 entries (F2_0_1_3)
F1_2 fishminiA - 8 entries (F1_2_2_3)
F0_100 fishminiA - 18 entries
F0_100 nsCOIFo - 6 entries (F0_100_1_1)
F0_100 mifish - 18 entries
F0_100 cep - 2 entries (F0_100_2_2 and F0_100_1_1)
F1_0 fishminiA - 16 entries (F0_0_1_1 and F0_0_1_2)


I hate the idea of doing this manually, but for the time being, it is nice to have eyes on some of these data before dropping replicates.

Interesting to note that FishminiA has the highest incidence of dissimilarity issues.


## Remove dissimilar replicates

```{r load-data}
# this is the dataframe that have been filtered by occupancy modeling and includes only those ASVs assigned to metazoan taxonomy
mock_feed_sodm_asvs_removed_dataframe

```


A clean, non-redundant version of the reference sample dataframe
```{r}
mockfeed_sodm_filtered_unique <- mock_feed_sodm_asvs_removed_dataframe %>% 
  select(locus, seq, sample, count) %>%
  unique() %>% # if there are multiple entries with different counts, we want to collapse those reads
  group_by(locus, seq, sample) %>%
  mutate(total_reads = sum(count)) %>%
  select(-count) %>%
  rename(count = total_reads)

```

So that is the dataframe from which we want to remove this particular list of locus-samples
```{r}
# read in the list of samples to remove
mockfeed_tossers <- read_csv("../data/mock_feed_replicates_to_remove.csv")

```

It turns out that an anti-join is all I need for this filtering step.
```{r}
mockfeed_sodm_bray_filtered_unique <- mockfeed_sodm_filtered_unique %>%
  anti_join(., mockfeed_tossers, by = c("locus", "sample")) 

# remove the negative controls from this df
mockfeed_decontaminated <- mockfeed_sodm_bray_filtered_unique %>%
  filter(!str_detect(sample, "NEG") & !str_detect(sample, "M_A")) # remove positive controls too
```

Save that
```{r save-decontaminated-df}
saveRDS(mockfeed_decontaminated, "../data/mockfeed_decontaminated.rds", compress = "xz")
```

Okay, so that is the dataset that I can work through the assessment analyses with, beginning with summary statistics, then adding in the taxonomy and assessing proportion fishmeal and % reads, etc.



### Taxonomy for those ASVs retained

```{r load-taxonomic-dfs}
# un-collapsed and collapsed versions of the taxonomy with a 98% identity threshold for species-level ID
tax_df_clean_slim <- readRDS("../data/uncollapsed_taxonomy_spp_98.rds")
clean_taxonomy_df_unique <- readRDS("../data/unique_taxonomy_spp_98.rds")

```

Adding back in the taxonomic information - uncollapsed, so there are multiple taxonomic entries for each locus/seq that need to be summarised.

```{r add-taxonomy}
# bind the complete (but filtered) taxonomic identities onto the ASV sequence information
mockfeed_taxonomy <- mockfeed_decontaminated %>%
  left_join(., tax_df_clean_slim, by = c("locus", "seq"))
  
```

Save that
```{r save-output}
# save output dataframe that has been filtered by Bray-Curtis as well as the occupancy modeling
saveRDS(mockfeed_taxonomy, "../data/mockfeed_taxonomy_sodm_bray_clean.rds", compress = "xz")

```
